1
|
Morrissey ZD, Gao J, Shetti A, Li W, Zhan L, Li W, Fortel I, Saido T, Saito T, Ajilore O, Cologna SM, Lazarov O, Leow AD. Temporal Alterations in White Matter in An App Knock-In Mouse Model of Alzheimer's Disease. eNeuro 2024; 11:ENEURO.0496-23.2024. [PMID: 38290851 PMCID: PMC10897532 DOI: 10.1523/eneuro.0496-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 01/05/2024] [Accepted: 01/17/2024] [Indexed: 02/01/2024] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia and results in neurodegeneration and cognitive impairment. White matter (WM) is affected in AD and has implications for neural circuitry and cognitive function. The trajectory of these changes across age, however, is still not well understood, especially at earlier stages in life. To address this, we used the AppNL-G-F/NL-G-F knock-in (APPKI) mouse model that harbors a single copy knock-in of the human amyloid precursor protein (APP) gene with three familial AD mutations. We performed in vivo diffusion tensor imaging (DTI) to study how the structural properties of the brain change across age in the context of AD. In late age APPKI mice, we observed reduced fractional anisotropy (FA), a proxy of WM integrity, in multiple brain regions, including the hippocampus, anterior commissure (AC), neocortex, and hypothalamus. At the cellular level, we observed greater numbers of oligodendrocytes in middle age (prior to observations in DTI) in both the AC, a major interhemispheric WM tract, and the hippocampus, which is involved in memory and heavily affected in AD, prior to observations in DTI. Proteomics analysis of the hippocampus also revealed altered expression of oligodendrocyte-related proteins with age and in APPKI mice. Together, these results help to improve our understanding of the development of AD pathology with age, and imply that middle age may be an important temporal window for potential therapeutic intervention.
Collapse
Affiliation(s)
- Zachery D Morrissey
- Graduate Program in Neuroscience, University of Illinois Chicago, Chicago, Illinois 60612
- Department of Psychiatry, University of Illinois Chicago, Chicago, Illinois 60612
- Department of Anatomy & Cell Biology, University of Illinois Chicago, Chicago, Illinois 60612
| | - Jin Gao
- Department of Electrical & Computer Engineering, University of Illinois Chicago, Chicago, Illinois 60607
- Preclinical Imaging Core, University of Illinois Chicago, Chicago, Illinois 60612
| | - Aashutosh Shetti
- Department of Anatomy & Cell Biology, University of Illinois Chicago, Chicago, Illinois 60612
| | - Wenping Li
- Department of Chemistry, University of Illinois Chicago, Chicago, Illinois 60607
| | - Liang Zhan
- Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261
| | - Weiguo Li
- Preclinical Imaging Core, University of Illinois Chicago, Chicago, Illinois 60612
- Department of Bioengineering, University of Illinois Chicago, Chicago, Illinois 60607
- Department of Radiology, Northwestern University, Chicago, Illinois 60611
| | - Igor Fortel
- Department of Bioengineering, University of Illinois Chicago, Chicago, Illinois 60607
| | - Takaomi Saido
- Laboratory for Proteolytic Neuroscience, RIKEN Center for Brain Science, Wako 351-0198, Japan
| | - Takashi Saito
- Department of Neurocognitive Science, Institute of Brain Science, Nagoya City University, Nagoya 467-8601, Japan
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois Chicago, Chicago, Illinois 60612
| | - Stephanie M Cologna
- Department of Chemistry, University of Illinois Chicago, Chicago, Illinois 60607
| | - Orly Lazarov
- Department of Anatomy & Cell Biology, University of Illinois Chicago, Chicago, Illinois 60612
| | - Alex D Leow
- Department of Psychiatry, University of Illinois Chicago, Chicago, Illinois 60612
- Department of Bioengineering, University of Illinois Chicago, Chicago, Illinois 60607
- Department of Computer Science, University of Illinois Chicago, Chicago, Illinois 60607
| |
Collapse
|
2
|
Nguyen TM, Leow AD, Ajilore O. A Review on Smartphone Keystroke Dynamics as a Digital Biomarker for Understanding Neurocognitive Functioning. Brain Sci 2023; 13:959. [PMID: 37371437 DOI: 10.3390/brainsci13060959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 06/07/2023] [Accepted: 06/14/2023] [Indexed: 06/29/2023] Open
Abstract
Can digital technologies provide a passive unobtrusive means to observe and study cognition outside of the laboratory? Previously, cognitive assessments and monitoring were conducted in a laboratory or clinical setting, allowing for a cross-sectional glimpse of cognitive states. In the last decade, researchers have been utilizing technological advances and devices to explore ways of assessing cognition in the real world. We propose that the virtual keyboard of smartphones, an increasingly ubiquitous digital device, can provide the ideal conduit for passive data collection to study cognition. Passive data collection occurs without the active engagement of a participant and allows for near-continuous, objective data collection. Most importantly, this data collection can occur in the real world, capturing authentic datapoints. This method of data collection and its analyses provide a more comprehensive and potentially more suitable insight into cognitive states, as intra-individual cognitive fluctuations over time have shown to be an early manifestation of cognitive decline. We review different ways passive data, centered around keystroke dynamics, collected from smartphones, have been used to assess and evaluate cognition. We also discuss gaps in the literature where future directions of utilizing passive data can continue to provide inferences into cognition and elaborate on the importance of digital data privacy and consent.
Collapse
Affiliation(s)
- Theresa M Nguyen
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Alex D Leow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
| |
Collapse
|
3
|
Ross MK, Tulabandhula T, Bennett CC, Baek E, Kim D, Hussain F, Demos AP, Ning E, Langenecker SA, Ajilore O, Leow AD. A Novel Approach to Clustering Accelerometer Data for Application in Passive Predictions of Changes in Depression Severity. Sensors (Basel) 2023; 23:1585. [PMID: 36772625 PMCID: PMC9920816 DOI: 10.3390/s23031585] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 01/11/2023] [Accepted: 01/29/2023] [Indexed: 06/18/2023]
Abstract
The treatment of mood disorders, which can become a lifelong process, varies widely in efficacy between individuals. Most options to monitor mood rely on subjective self-reports and clinical visits, which can be burdensome and may not portray an accurate representation of what the individual is experiencing. A passive method to monitor mood could be a useful tool for those with these disorders. Some previously proposed models utilized sensors from smartphones and wearables, such as the accelerometer. This study examined a novel approach of processing accelerometer data collected from smartphones only while participants of the open-science branch of the BiAffect study were typing. The data were modeled by von Mises-Fisher distributions and weighted networks to identify clusters relating to different typing positions unique for each participant. Longitudinal features were derived from the clustered data and used in machine learning models to predict clinically relevant changes in depression from clinical and typing measures. Model accuracy was approximately 95%, with 97% area under the ROC curve (AUC). The accelerometer features outperformed the vast majority of clinical and typing features, which suggested that this new approach to analyzing accelerometer data could contribute towards unobtrusive detection of changes in depression severity without the need for clinical input.
Collapse
Affiliation(s)
- Mindy K. Ross
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Theja Tulabandhula
- Department of Information and Decision Sciences, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Casey C. Bennett
- Department of Intelligence Computing, Hanyang University, Seoul 04763, Republic of Korea
- Department of Computing, DePaul University, Chicago, IL 60604, USA
| | - EuGene Baek
- Department of Intelligence Computing, Hanyang University, Seoul 04763, Republic of Korea
| | - Dohyeon Kim
- Department of Intelligence Computing, Hanyang University, Seoul 04763, Republic of Korea
| | - Faraz Hussain
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Alexander P. Demos
- Department of Psychology, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Emma Ning
- Department of Psychology, University of Illinois at Chicago, Chicago, IL 60612, USA
| | | | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Alex D. Leow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60612, USA
| |
Collapse
|
4
|
Morrissey ZD, Gao J, Zhan L, Li W, Fortel I, Saido T, Saito T, Bakker A, Mackin S, Ajilore O, Lazarov O, Leow AD. Hippocampal functional connectivity across age in an App knock-in mouse model of Alzheimer's disease. Front Aging Neurosci 2023; 14:1085989. [PMID: 36711209 PMCID: PMC9878347 DOI: 10.3389/fnagi.2022.1085989] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/28/2022] [Indexed: 01/15/2023] Open
Abstract
Introduction Alzheimer's disease (AD) is a progressive neurodegenerative disease. The early processes of AD, however, are not fully understood and likely begin years before symptoms manifest. Importantly, disruption of the default mode network, including the hippocampus, has been implicated in AD. Methods To examine the role of functional network connectivity changes in the early stages of AD, we performed resting-state functional magnetic resonance imaging (rs-fMRI) using a mouse model harboring three familial AD mutations (App NL-G-F/NL-G-F knock-in, APPKI) in female mice in early, middle, and late age groups. The interhemispheric and intrahemispheric functional connectivity (FC) of the hippocampus was modeled across age. Results We observed higher interhemispheric functional connectivity (FC) in the hippocampus across age. This was reduced, however, in APPKI mice in later age. Further, we observed loss of hemispheric asymmetry in FC in APPKI mice. Discussion Together, this suggests that there are early changes in hippocampal FC prior to heavy onset of amyloid β plaques, and which may be clinically relevant as an early biomarker of AD.
Collapse
Affiliation(s)
- Zachery D. Morrissey
- Graduate Program in Neuroscience, University of Illinois at Chicago, Chicago, IL, United States
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
- Department of Anatomy & Cell Biology, University of Illinois at Chicago, Chicago, IL, United States
| | - Jin Gao
- Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL, United States
- Preclinical Imaging Core, University of Illinois at Chicago, Chicago, IL, United States
| | - Liang Zhan
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Weiguo Li
- Preclinical Imaging Core, University of Illinois at Chicago, Chicago, IL, United States
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States
- Department of Radiology, Northwestern University, Chicago, IL, United States
| | - Igor Fortel
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States
| | - Takaomi Saido
- Laboratory for Proteolytic Neuroscience, RIKEN Center for Brain Science, Wako, Japan
| | - Takashi Saito
- Department of Neurocognitive Science, Institute of Brain Science, Nagoya City University, Nagoya, Japan
| | - Arnold Bakker
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, United States
- Department of Neurology, Johns Hopkins University, Baltimore, MD, United States
| | - Scott Mackin
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
| | - Orly Lazarov
- Department of Anatomy & Cell Biology, University of Illinois at Chicago, Chicago, IL, United States
| | - Alex D. Leow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States
| |
Collapse
|
5
|
Tang H, Guo L, Fu X, Wang Y, Mackin S, Ajilore O, Leow AD, Thompson PM, Huang H, Zhan L. Signed graph representation learning for functional-to-structural brain network mapping. Med Image Anal 2023; 83:102674. [PMID: 36442294 PMCID: PMC9904311 DOI: 10.1016/j.media.2022.102674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 10/04/2022] [Accepted: 10/27/2022] [Indexed: 11/18/2022]
Abstract
MRI-derived brain networks have been widely used to understand functional and structural interactions among brain regions, and factors that affect them, such as brain development and diseases. Graph mining on brain networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. Since brain functional and structural networks describe the brain topology from different perspectives, exploring a representation that combines these cross-modality brain networks has significant clinical implications. Most current studies aim to extract a fused representation by projecting the structural network to the functional counterpart. Since the functional network is dynamic and the structural network is static, mapping a static object to a dynamic object may not be optimal. However, mapping in the opposite direction (i.e., from functional to structural networks) are suffered from the challenges introduced by negative links within signed graphs. Here, we propose a novel graph learning framework, named as Deep Signed Brain Graph Mining or DSBGM, with a signed graph encoder that, from an opposite perspective, learns the cross-modality representations by projecting the functional network to the structural counterpart. We validate our framework on clinical phenotype and neurodegenerative disease prediction tasks using two independent, publicly available datasets (HCP and OASIS). Our experimental results clearly demonstrate the advantages of our model compared to several state-of-the-art methods.
Collapse
Affiliation(s)
- Haoteng Tang
- University of Pittsburgh, 3700 O'Hara St., Pittsburgh, 15261, PA, USA.
| | - Lei Guo
- University of Pittsburgh, 3700 O'Hara St., Pittsburgh, 15261, PA, USA
| | - Xiyao Fu
- University of Pittsburgh, 3700 O'Hara St., Pittsburgh, 15261, PA, USA
| | - Yalin Wang
- Arizona State University, 699 S Mill Ave., Tempe, 85281, AZ, USA
| | - Scott Mackin
- University of California San Francisco, 505 Parnassus Ave., San Francisco, 94143, CA, USA
| | - Olusola Ajilore
- University of Illinois Chicago, 1601 W. Taylor St., Chicago, 60612, IL, USA
| | - Alex D Leow
- University of Illinois Chicago, 1601 W. Taylor St., Chicago, 60612, IL, USA
| | - Paul M Thompson
- University of Southern California, 2001 N. Soto St., Los Angeles, 90032, CA, USA
| | - Heng Huang
- University of Pittsburgh, 3700 O'Hara St., Pittsburgh, 15261, PA, USA
| | - Liang Zhan
- University of Pittsburgh, 3700 O'Hara St., Pittsburgh, 15261, PA, USA.
| |
Collapse
|
6
|
Bennett CC, Ross MK, Baek E, Kim D, Leow AD. Smartphone accelerometer data as a proxy for clinical data in modeling of bipolar disorder symptom trajectory. NPJ Digit Med 2022; 5:181. [PMID: 36517582 PMCID: PMC9751066 DOI: 10.1038/s41746-022-00741-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 12/02/2022] [Indexed: 12/23/2022] Open
Abstract
Being able to track and predict fluctuations in symptoms of mental health disorders such as bipolar disorder outside the clinic walls is critical for expanding access to care for the global population. To that end, we analyze a dataset of 291 individuals from a smartphone app targeted at bipolar disorder, which contains rich details about their smartphone interactions (including typing dynamics and accelerometer motion) collected everyday over several months, along with more traditional clinical features. The aim is to evaluate whether smartphone accelerometer data could serve as a proxy for traditional clinical data, either by itself or in combination with typing dynamics. Results show that accelerometer data improves the predictive performance of machine learning models by nearly 5% over those previously reported in the literature based only on clinical data and typing dynamics. This suggests it is possible to elicit essentially the same "information" about bipolar symptomology using different data sources, in a variety of settings.
Collapse
Affiliation(s)
- Casey C. Bennett
- grid.49606.3d0000 0001 1364 9317Department of Intelligence Computing, Hanyang University, Seoul, Korea ,grid.254920.80000 0001 0707 2013Department of Computing, DePaul University, Chicago, IL USA
| | - Mindy K. Ross
- grid.185648.60000 0001 2175 0319Department of Psychiatry, University of Illinois–Chicago, Chicago, IL USA
| | - EuGene Baek
- grid.49606.3d0000 0001 1364 9317Department of Intelligence Computing, Hanyang University, Seoul, Korea
| | - Dohyeon Kim
- grid.49606.3d0000 0001 1364 9317Department of Intelligence Computing, Hanyang University, Seoul, Korea
| | - Alex D. Leow
- grid.185648.60000 0001 2175 0319Department of Psychiatry, University of Illinois–Chicago, Chicago, IL USA ,grid.185648.60000 0001 2175 0319Dept. of Biomedical Engineering, University of Illinois–Chicago, Chicago, IL USA
| |
Collapse
|
7
|
Tang H, Guo L, Fu X, Qu B, Ajilore O, Wang Y, Thompson PM, Huang H, Leow AD, Zhan L. A Hierarchical Graph Learning Model for Brain Network Regression Analysis. Front Neurosci 2022; 16:963082. [PMID: 35903810 PMCID: PMC9315240 DOI: 10.3389/fnins.2022.963082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 06/22/2022] [Indexed: 11/29/2022] Open
Abstract
Brain networks have attracted increasing attention due to the potential to better characterize brain dynamics and abnormalities in neurological and psychiatric conditions. Recent years have witnessed enormous successes in deep learning. Many AI algorithms, especially graph learning methods, have been proposed to analyze brain networks. An important issue for existing graph learning methods is that those models are not typically easy to interpret. In this study, we proposed an interpretable graph learning model for brain network regression analysis. We applied this new framework on the subjects from Human Connectome Project (HCP) for predicting multiple Adult Self-Report (ASR) scores. We also use one of the ASR scores as the example to demonstrate how to identify sex differences in the regression process using our model. In comparison with other state-of-the-art methods, our results clearly demonstrate the superiority of our new model in effectiveness, fairness, and transparency.
Collapse
Affiliation(s)
- Haoteng Tang
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Lei Guo
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Xiyao Fu
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Benjamin Qu
- Mission San Jose High School, Fremont, CA, United States
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois Chicago, Chicago, IL, United States
| | - Yalin Wang
- Department of Computer Science and Engineering, Arizona State University, Tempe, AZ, United States
| | - Paul M. Thompson
- Imaging Genetics Center, University of Southern California, Los Angeles, CA, United States
| | - Heng Huang
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Alex D. Leow
- Department of Psychiatry, University of Illinois Chicago, Chicago, IL, United States
| | - Liang Zhan
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
- *Correspondence: Liang Zhan
| |
Collapse
|
8
|
Nebeker C, Leow AD, Moore RC. From Return of Information to Return of Value: Ethical Considerations when Sharing Individual-Level Research Data. J Alzheimers Dis 2020; 71:1081-1088. [PMID: 31524169 DOI: 10.3233/jad-190589] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The implementation of digital health technologies into research studies for Alzheimer's disease and other clinical populations is on the rise. Digital tools and strategies create opportunities to further expand the framework for conducting research beyond the traditional medical research model. The combination of participatory and community-based research methods, electronic health records, and the creation of multi-dimensional, large-scale research platforms to support precision medicine, along with the Internet of Things era, have led to more engaged and informed research participants. Research participants increasingly possess an expectation they will play a critical role as partners in the design and conduct of research. Moreover, there is growing interest among research participants to have access to individual-level research data in real-time and/or at study completion. The traditional medical research model is largely one-directional where participants contribute data that is analyzed by researchers to yield generalizable knowledge. In this Ethics Review, we discuss a framework for a more nuanced intermediate research model, which is largely bidirectional and individually customized. Based on the seven ethical guidelines adopted by the National Institutes of Health, we speak to the ethical challenges of this intermediate type research. We also introduce a concept we are calling "MyTerms," in which prospective participants tailor the terms and conditions of informed consent to their personalized preferences for receiving information, including research results. Digital health technologies offer a convenient and flexible approach for researchers to develop protocols that make it possible for participants to obtain access to their study data in a personalized and meaningful way.
Collapse
Affiliation(s)
- Camille Nebeker
- Center for Wireless and Population Health Systems, UC San Diego, La Jolla, CA, USA.,Department of Family Medicine and Public Health, School of Medicine, UC San Diego, La Jolla, CA, USA.,Research Center for Optimal Digital Ethics, Qualcomm Institute and School of Medicine, UC San Diego, La Jolla, CA, USA
| | - Alex D Leow
- Departments of Psychiatry and BioEngineering, University of Illinois College of Medicine, Chicago, IL, USA
| | - Raeanne C Moore
- Center for Wireless and Population Health Systems, UC San Diego, La Jolla, CA, USA.,Department of Psychiatry, School of Medicine, UC San Diego, La Jolla, CA, USA.,Mental Health Technology Center, UC San Diego, La Jolla, CA, USA
| |
Collapse
|
9
|
Chen Y, Tang H, Guo L, Peven JC, Huang H, Leow AD, Lamar M, Zhan L. A GENERALIZED FRAMEWORK OF PATHLENGTH ASSOCIATED COMMUNITY ESTIMATION FOR BRAIN STRUCTURAL NETWORK. Proc IEEE Int Symp Biomed Imaging 2020; 2020:288-291. [PMID: 33173559 DOI: 10.1109/isbi45749.2020.9098552] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Diffusion MRI-derived brain structural network has been widely used in brain research and community or modular structure is one of popular network features, which can be extracted from network edge-derived pathlengths. Conceptually, brain structural network edges represent the connecting strength between pair of nodes, thus non-negative. The pathlength. Many studies have demonstrated that each brain network edge can be affected by many confounding factors (e.g. age, sex, etc.) and this influence varies on each edge. However, after applying generalized linear regression to remove those confounding's effects, some network edges may become negative, which leads to barriers in extracting the community structure. In this study, we propose a novel generalized framework to solve this negative edge issue in extracting the modular structure from brain structural network. We have compared our framework with traditional Q method. The results clearly demonstrated that our framework has significant advantages in both stability and sensitivity.
Collapse
Affiliation(s)
- Yurong Chen
- Department of Electrical and Computer Engineering, University of Pittsburgh, PA, USA
| | - Haoteng Tang
- Department of Electrical and Computer Engineering, University of Pittsburgh, PA, USA
| | - Lei Guo
- Department of Electrical and Computer Engineering, University of Pittsburgh, PA, USA
| | - Jamie C Peven
- Department of Psychology, University of Pittsburgh, PA, USA
| | - Heng Huang
- Department of Electrical and Computer Engineering, University of Pittsburgh, PA, USA
| | - Alex D Leow
- Department of Psychiatry, University of Illinois at Chicago, IL, USA
| | - Melissa Lamar
- Rush Alzheimer's Disease Center, Rush University Medical Center, IL, USA.,Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, IL, USA
| | - Liang Zhan
- Department of Electrical and Computer Engineering, University of Pittsburgh, PA, USA
| |
Collapse
|
10
|
Abstract
Current management of psychiatric disorders relies heavily on retrospective, subjective reports provided by patients and their families. Consequently, psychiatric services are often provisioned inefficiently and with suboptimal outcomes. Recent advances in computing and sensor technologies have enabled the development of real-time monitoring systems for the diagnosis and management of psychiatric disorders. The state of these technologies is rapidly evolving, with passive monitoring and predictive modeling as two areas that have great potential to affect psychiatric care. Although outpatient psychiatry probably stands to benefit the most from the use of real-time monitoring technologies, there are also several ways in which inpatient psychiatry may also benefit. As the capabilities of these technologies increase and their use becomes more common, many ethical and legal issues will need to be considered. The role of governmental regulatory bodies and nongovernmental organizations in providing oversight of the implementation of these technologies is an active area of discussion.
Collapse
Affiliation(s)
- John Zulueta
- Department of Psychiatry, College of Medicine (all authors), and Department of Bioengineering and Computer Science, College of Engineering (Leow), all at the University of Illinois at Chicago
| | - Alex D Leow
- Department of Psychiatry, College of Medicine (all authors), and Department of Bioengineering and Computer Science, College of Engineering (Leow), all at the University of Illinois at Chicago
| | - Olusola Ajilore
- Department of Psychiatry, College of Medicine (all authors), and Department of Bioengineering and Computer Science, College of Engineering (Leow), all at the University of Illinois at Chicago
| |
Collapse
|
11
|
Vaughn DA, Kerr WT, Moody TD, Cheng GK, Morfini F, Zhang A, Leow AD, Strober MA, Cohen MS, Feusner JD. Differentiating weight-restored anorexia nervosa and body dysmorphic disorder using neuroimaging and psychometric markers. PLoS One 2019; 14:e0213974. [PMID: 31059514 PMCID: PMC6502309 DOI: 10.1371/journal.pone.0213974] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 03/05/2019] [Indexed: 12/22/2022] Open
Abstract
Anorexia nervosa (AN) and body dysmorphic disorder (BDD) are potentially life-threatening conditions whose partially overlapping phenomenology—distorted perception of appearance, obsessions/compulsions, and limited insight—can make diagnostic distinction difficult in some cases. Accurate diagnosis is crucial, as the effective treatments for AN and BDD differ. To improve diagnostic accuracy and clarify the contributions of each of the multiple underlying factors, we developed a two-stage machine learning model that uses multimodal, neurobiology-based, and symptom-based quantitative data as features: task-based functional magnetic resonance imaging data using body visual stimuli, graph theory metrics of white matter connectivity from diffusor tensor imaging, and anxiety, depression, and insight psychometric scores. In a sample of unmedicated adults with BDD (n = 29), unmedicated adults with weight-restored AN (n = 24), and healthy controls (n = 31), the resulting model labeled individuals with an accuracy of 76%, significantly better than the chance accuracy of 35% ( p^<10‑4). In the multivariate model, reduced white matter global efficiency and better insight were associated more with AN than with BDD. These results improve our understanding of the relative contributions of the neurobiological characteristics and symptoms of these disorders. Moreover, this approach has the potential to aid clinicians in diagnosis, thereby leading to more tailored therapy.
Collapse
Affiliation(s)
- Don A. Vaughn
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, California, United States of America
| | - Wesley T. Kerr
- David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Biomathematics, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Internal Medicine, Eisenhower Medical Center, Rancho Mirage, California, United States of America
| | - Teena D. Moody
- Department of Psychology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Gigi K. Cheng
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, California, United States of America
| | - Francesca Morfini
- Department of Psychology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Aifeng Zhang
- Department of Psychiatry, University of Illinois College of Medicine, Chicago, Illinois, United States of America
| | - Alex D. Leow
- Department of Psychiatry, University of Illinois College of Medicine, Chicago, Illinois, United States of America
| | - Michael A. Strober
- Department of Psychology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Mark S. Cohen
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Psychology, Harvard University, Cambridge, Massachusetts, United States of America
- Departments of Neurology, Radiology, Biomedical Physics, Psychology, Bioengineering and California Nanosystems Institute, University of California Los Angeles, Los Angeles, California, United States of America
| | - Jamie D. Feusner
- Department of Psychology, Harvard University, Cambridge, Massachusetts, United States of America
- * E-mail:
| |
Collapse
|
12
|
Keiriz JJG, Zhan L, Ajilore O, Leow AD, Forbes AG. NeuroCave: A web-based immersive visualization platform for exploring connectome datasets. Netw Neurosci 2018; 2:344-361. [PMID: 30294703 PMCID: PMC6145855 DOI: 10.1162/netn_a_00044] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Accepted: 01/10/2018] [Indexed: 12/11/2022] Open
Abstract
We introduce NeuroCave, a novel immersive visualization system that facilitates the visual inspection of structural and functional connectome datasets. The representation of the human connectome as a graph enables neuroscientists to apply network-theoretic approaches in order to explore its complex characteristics. With NeuroCave, brain researchers can interact with the connectome-either in a standard desktop environment or while wearing portable virtual reality headsets (such as Oculus Rift, Samsung Gear, or Google Daydream VR platforms)-in any coordinate system or topological space, as well as cluster brain regions into different modules on-demand. Furthermore, a default side-by-side layout enables simultaneous, synchronized manipulation in 3D, utilizing modern GPU hardware architecture, and facilitates comparison tasks across different subjects or diagnostic groups or longitudinally within the same subject. Visual clutter is mitigated using a state-of-the-art edge bundling technique and through an interactive layout strategy, while modular structure is optimally positioned in 3D exploiting mathematical properties of platonic solids. NeuroCave provides new functionality to support a range of analysis tasks not available in other visualization software platforms.
Collapse
Affiliation(s)
- Johnson J. G. Keiriz
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
- Collaborative Neuroimaging Environment for Connectomics, University of Illinois Chicago, Chicago, IL, USA
| | - Liang Zhan
- Department of Engineering and Technology, University of Wisconsin–Stout Menomonie, WI, USA
- Collaborative Neuroimaging Environment for Connectomics, University of Illinois Chicago, Chicago, IL, USA
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
- Collaborative Neuroimaging Environment for Connectomics, University of Illinois Chicago, Chicago, IL, USA
| | - Alex D. Leow
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
- Collaborative Neuroimaging Environment for Connectomics, University of Illinois Chicago, Chicago, IL, USA
| | - Angus G. Forbes
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
- Collaborative Neuroimaging Environment for Connectomics, University of Illinois Chicago, Chicago, IL, USA
- Computational Media Department, University of California, Santa Cruz, Santa Cruz, CA, USA
| |
Collapse
|
13
|
Xing S, Kim S, Schumock GT, Touchette DR, Calip GS, Leow AD, Lee TA. Risk of Diabetes Hospitalization or Diabetes Drug Intensification in Patients With Depression and Diabetes Using Second-Generation Antipsychotics Compared to Other Depression Therapies. Prim Care Companion CNS Disord 2018; 20. [PMID: 29873957 DOI: 10.4088/pcc.17m02220] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Accepted: 12/18/2017] [Indexed: 10/16/2022] Open
Abstract
Objective Use of second-generation antipsychotics (SGAs) for treatment of depression has increased, and patients with depression and comorbid diabetes or cardiovascular disease are more likely to use SGAs than those without these conditions. We compared SGA and non-SGA depression pharmacotherapies on the risk of diabetes hospitalization or treatment intensification in adults with depression and preexisting diabetes. Methods This was a retrospective cohort study of US commercially insured adults (2009-2015 Truven MarketScan Commercial Claims and Encounters Database) aged 18-64 years old with type 2 diabetes mellitus and unipolar depression previously treated with a selective serotonin reuptake inhibitor or serotonin-norepinephrine reuptake inhibitor. New users of SGAs versus non-SGAs, as well as specific treatments (aripiprazole, quetiapine, bupropion, mirtazapine, and tricyclic antidepressants [TCAs]) were matched on class/medication-specific high-dimensional propensity score. Cox proportional hazard models were used to compare the risk of diabetes-related hospitalization or treatment intensification. Results We identified 6,625 SGA (aripiprazole = 3,461; quetiapine = 1,977; other = 1,187) and 23,921 non-SGA patients for inclusion (bupropion = 15,511; mirtazapine = 1,837; TCAs = 5,989; other = 584) with a mean age of 51 years. In the matched cohort, the rate of diabetes-related hospitalization or drug intensification was 47.9 per 100 person-years in the SGA group and 43.5 per 100 person-years in the non-SGA group (adjusted hazard ratio [aHR] = 1.03; 95% CI, 0.96-1.11). When comparing treatment subgroups, the risk of events was lower for bupropion versus TCAs (aHR = 0.85; 95% CI, 0.76-0.98), quetiapine versus mirtazapine (aHR = 0.82; 95% CI, 0.67-0.99), and quetiapine versus TCAs (aHR = 0.84; 95% CI, 0.72-0.98). For other comparisons, differences were small and not statistically significant. Conclusions While drug-specific effects on risk of diabetes hospitalization or treatment intensification most likely guide clinical decision making, we observed only modest differences in risk. The overall impact of SGAs on diabetes control depends not only on direct effects on glucose metabolism but also on effectiveness of depression symptom relief. Future studies evaluating other diabetes outcomes (glycosylated hemoglobin, diabetes complications) are needed.
Collapse
Affiliation(s)
- Shan Xing
- Department of Pharmacy Systems, Outcomes and Policy, College of Pharmacy, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Shiyun Kim
- Department of Pharmacy Practice, College of Pharmacy, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Glen T Schumock
- Department of Pharmacy Systems, Outcomes and Policy, College of Pharmacy, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Daniel R Touchette
- Department of Pharmacy Systems, Outcomes and Policy, College of Pharmacy, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Gregory S Calip
- Department of Pharmacy Systems, Outcomes and Policy, College of Pharmacy, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Alex D Leow
- Department of Psychiatry, College of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA.,Department of Bioengineering, College of Engineering and College of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Todd A Lee
- Department of Pharmacy Systems, Outcomes and Policy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St, Room 287, MC 871, Chicago, IL 60612. .,Department of Pharmacy Systems, Outcomes and Policy, College of Pharmacy, University of Illinois at Chicago, Chicago, Illinois, USA
| |
Collapse
|
14
|
Xing S, Calip GS, Leow AD, Kim S, Schumock GT, Touchette DR, Lee TA. The impact of depression medications on oral antidiabetic drug adherence in patients with diabetes and depression. J Diabetes Complications 2018; 32:492-500. [PMID: 29544744 PMCID: PMC5920707 DOI: 10.1016/j.jdiacomp.2017.12.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Revised: 11/29/2017] [Accepted: 12/21/2017] [Indexed: 12/13/2022]
Abstract
AIMS To compare adherence and persistence to oral antidiabetic drugs (OAD) between patients who are new users of second generation antipsychotics (SGA) versus new users of other depression therapies in adults with type 2 diabetes mellitus (T2DM) and major depressive disorder (MDD). METHODS Adults 18-64 years with previously-treated T2DM and MDD (past OAD and SSRI/SNRI use) who are new users of SGA or non-SGA therapies (bupropion, lithium, mirtazapine, thyroid hormone, tricyclic antidepressant) were identified in the 2009-2015 MarketScan® Commercial Claims and Encounters database. Multivariate regression models were used to determine the odds of a ≥10% decline in OAD adherence over 180- and 365-days, and time to OAD discontinuation, adjusting for differences between groups. RESULTS A total of 8664 (21.5% SGA), 8311 (22.1% SGA), and 17,524 (21.3% SGA) patients met inclusion criteria for the 180-day adherence, 365-day adherence, and persistence cohorts, respectively. Over 180-days, 16.6% of SGA and 13.3% of non-SGA initiators had a ≥10% decline in OAD adherence (adjusted odds ratio [OR] = 1.41, 95% CI 1.21-1.63). Over 365-days, 22.3% of SGA and 18.9% of non-SGA initiators had a ≥ 10% decline (OR = 1.34, 95% CI 1.17-1.53). Time to OAD discontinuation was similar between groups (adjusted hazard ratio = 1.03, 95% CI 0.94-1.12). CONCLUSION Use of SGA was associated with a 1.3-1.4 times higher odds of a ≥10% decline in OAD adherence. Adherence to OAD is critical for optimal diabetes control and reductions in this magnitude may impact A1C. Close monitoring of OAD adherence after SGA initiation is warranted.
Collapse
Affiliation(s)
- Shan Xing
- University of Illinois at Chicago, Department of Pharmacy, Systems, Outcomes and Policy, College of Pharmacy, United States
| | - Gregory S Calip
- University of Illinois at Chicago, Department of Pharmacy, Systems, Outcomes and Policy, College of Pharmacy, United States
| | - Alex D Leow
- University of Illinois at Chicago, Department of Psychiatry, College of Medicine, United States; University of Illinois at Chicago, Department of Bioengineering, College of Engineering, College of Medicine, United States
| | - Shiyun Kim
- University of Illinois at Chicago, Department of Pharmacy Practice, College of Pharmacy, United States
| | - Glen T Schumock
- University of Illinois at Chicago, Department of Pharmacy, Systems, Outcomes and Policy, College of Pharmacy, United States
| | - Daniel R Touchette
- University of Illinois at Chicago, Department of Pharmacy, Systems, Outcomes and Policy, College of Pharmacy, United States
| | - Todd A Lee
- University of Illinois at Chicago, Department of Pharmacy, Systems, Outcomes and Policy, College of Pharmacy, United States.
| |
Collapse
|
15
|
Conrin SD, Zhan L, Morrissey ZD, Xing M, Forbes A, Maki P, Milad MR, Ajilore O, Langenecker SA, Leow AD. From Default Mode Network to the Basal Configuration: Sex Differences in the Resting-State Brain Connectivity as a Function of Age and Their Clinical Correlates. Front Psychiatry 2018; 9:365. [PMID: 30150944 PMCID: PMC6100484 DOI: 10.3389/fpsyt.2018.00365] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2018] [Accepted: 07/23/2018] [Indexed: 11/13/2022] Open
Abstract
Connectomics is a framework that models brain structure and function interconnectivity as a network, rather than narrowly focusing on select regions-of-interest. MRI-derived connectomes can be structural, usually based on diffusion-weighted MR imaging, or functional, usually formed by examining fMRI blood-oxygen-level-dependent (BOLD) signal correlations. Recently, we developed a novel method for assessing the hierarchical modularity of functional brain networks-the probability associated community estimation (PACE). PACE uniquely permits a dual formulation, thus yielding equivalent connectome modular structure regardless of whether positive or negative edges are considered. This method was rigorously validated using the 1,000 functional connectomes project data set (F1000, RRID:SCR_005361) (1) and the Human Connectome Project (HCP, RRID:SCR_006942) (2, 3) and we reported novel sex differences in resting-state connectivity not previously reported. (4) This study further examines sex differences in regard to hierarchical modularity as a function of age and clinical correlates, with findings supporting a basal configuration framework as a more nuanced and dynamic way of conceptualizing the resting-state connectome that is modulated by both age and sex. Our results showed that differences in connectivity between men and women in the 22-25 age range were not significantly different. However, these same non-significant differences attained significance in both the 26-30 age group (p = 0.003) and the 31-35 age group (p < 0.001). At the most global level, areas of diverging sex difference include parts of the prefrontal cortex and the temporal lobe, amygdala, hippocampus, inferior parietal lobule, posterior cingulate, and precuneus. Further, we identified statistically different self-reported summary scores of inattention, hyperactivity, and anxiety problems between men and women. These self-reports additionally divergently interact with age and the basal configuration between sexes.
Collapse
Affiliation(s)
- Sean D Conrin
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
| | - Liang Zhan
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States.,Department of Engineering and Technology, University of Wisconsin-Stout, Menomonie, WI, United States
| | - Zachery D Morrissey
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
| | - Mengqi Xing
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States.,Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States
| | - Angus Forbes
- Department of Computational Media, University of California, Santa Cruz, Santa Cruz, CA, United States
| | - Pauline Maki
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
| | - Mohammed R Milad
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
| | - Scott A Langenecker
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
| | - Alex D Leow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States.,Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States
| |
Collapse
|
16
|
Zhan L, Jenkins LM, Wolfson OE, GadElkarim JJ, Nocito K, Thompson PM, Ajilore OA, Chung MK, Leow AD. The significance of negative correlations in brain connectivity. J Comp Neurol 2017; 525:3251-3265. [PMID: 28675490 PMCID: PMC6625529 DOI: 10.1002/cne.24274] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Revised: 06/25/2017] [Accepted: 06/26/2017] [Indexed: 11/05/2022]
Abstract
Understanding the modularity of functional magnetic resonance imaging (fMRI)-derived brain networks or "connectomes" can inform the study of brain function organization. However, fMRI connectomes additionally involve negative edges, which may not be optimally accounted for by existing approaches to modularity that variably threshold, binarize, or arbitrarily weight these connections. Consequently, many existing Q maximization-based modularity algorithms yield variable modular structures. Here, we present an alternative complementary approach that exploits how frequent the blood-oxygen-level-dependent (BOLD) signal correlation between two nodes is negative. We validated this novel probability-based modularity approach on two independent publicly-available resting-state connectome data sets (the Human Connectome Project [HCP] and the 1,000 functional connectomes) and demonstrated that negative correlations alone are sufficient in understanding resting-state modularity. In fact, this approach (a) permits a dual formulation, leading to equivalent solutions regardless of whether one considers positive or negative edges; (b) is theoretically linked to the Ising model defined on the connectome, thus yielding modularity result that maximizes data likelihood. Additionally, we were able to detect novel and consistent sex differences in modularity in both data sets. As data sets like HCP become widely available for analysis by the neuroscience community at large, alternative and perhaps more advantageous computational tools to understand the neurobiological information of negative edges in fMRI connectomes are increasingly important.
Collapse
Affiliation(s)
- Liang Zhan
- Computer Engineering Program, University of Wisconsin-Stout, Menomonie, Wisconsin
| | | | - Ouri E. Wolfson
- Department of Computer Science, University of Illinois, Chicago, Illinois
| | | | - Kevin Nocito
- Department of Bioengineering, University of Illinois, Chicago, Illinois
| | - Paul M. Thompson
- Imaging Genetics Center, and Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, Marina del Rey, California
| | | | - Moo K. Chung
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Alex D. Leow
- Department of Psychiatry, University of Illinois, Chicago, Illinois
- Department of Computer Science, University of Illinois, Chicago, Illinois
- Department of Bioengineering, University of Illinois, Chicago, Illinois
| |
Collapse
|
17
|
Zhan L, Jenkins LM, Zhang A, Conte G, Forbes A, Harvey D, Angkustsiri K, Goodrich-Hunsaker NJ, Durdle C, Lee A, Schumann C, Carmichael O, Kalish K, Leow AD, Simon TJ. Baseline connectome modular abnormalities in the childhood phase of a longitudinal study on individuals with chromosome 22q11.2 deletion syndrome. Hum Brain Mapp 2017; 39:232-248. [PMID: 28990258 DOI: 10.1002/hbm.23838] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Revised: 09/20/2017] [Accepted: 09/27/2017] [Indexed: 01/09/2023] Open
Abstract
Occurring in at least 1 in 3,000 live births, chromosome 22q11.2 deletion syndrome (22q11DS) produces a complex phenotype that includes a constellation of medical complications such as congenital cardiac defects, immune deficiency, velopharyngeal dysfunction, and characteristic facial dysmorphic features. There is also an increased incidence of psychiatric diagnosis, especially intellectual disability and ADHD in childhood, lifelong anxiety, and a strikingly high rate of schizophrenia spectrum disorders, which occur in around 30% of adults with 22q11DS. Using innovative computational connectomics, we studied how 22q11DS affects high-level network signatures of hierarchical modularity and its intrinsic geometry in 55 children with confirmed 22q11DS and 27 Typically Developing (TD) children. Results identified 3 subgroups within our 22q11DS sample using a K-means clustering approach based on several midline structural measures-of-interests. Each subgroup exhibited distinct patterns of connectome abnormalities. Subtype 1, containing individuals with generally healthy-looking brains, exhibited no significant differences in either modularity or intrinsic geometry when compared with TD. By contrast, the more anomalous 22q11DS Subtypes 2 and 3 brains revealed significant modular differences in the right hemisphere, while Subtype 3 (the most anomalous anatomy) further exhibited significantly abnormal connectome intrinsic geometry in the form of left-right temporal disintegration. Taken together, our findings supported an overall picture of (a) anterior-posteriorly differential interlobar frontotemporal/frontoparietal dysconnectivity in Subtypes 2 and 3 and (b) differential intralobar dysconnectivity in Subtype 3. Our ongoing studies are focusing on whether these subtypes and their connnectome signatures might be valid biomarkers for predicting the degree of psychosis-proneness risk found in 22q11DS. Hum Brain Mapp 39:232-248, 2018. © 2017 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Liang Zhan
- Computer Engineering Program, University of Wisconsin-Stout, Wisconsin
| | | | - Aifeng Zhang
- Department of Psychiatry, University of Illinois, Chicago, Illinois
| | - Giorgio Conte
- Department of Computer Science, University of Illinois, Chicago, Illinois
| | - Angus Forbes
- Department of Computer Science, University of Illinois, Chicago, Illinois
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, School of Medicine, University of California, Davis, California
| | | | - Naomi J Goodrich-Hunsaker
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, California.,Department of Psychology, Brigham Young University, Provo, Utah
| | - Courtney Durdle
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, California
| | - Aaron Lee
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, California
| | - Cyndi Schumann
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, California
| | - Owen Carmichael
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana
| | - Kristopher Kalish
- Department of Neurology, University of California, Davis, California
| | - Alex D Leow
- Department of Psychiatry, University of Illinois, Chicago, Illinois.,Department of Bioengineering, University of Illinois, Chicago, Illinois
| | - Tony J Simon
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, California
| |
Collapse
|
18
|
Nir TM, Jahanshad N, Villalon-Reina JE, Isaev D, Zavaliangos-Petropulu A, Zhan L, Leow AD, Jack CR, Weiner MW, Thompson PM. Fractional anisotropy derived from the diffusion tensor distribution function boosts power to detect Alzheimer's disease deficits. Magn Reson Med 2017; 78:2322-2333. [PMID: 28266059 DOI: 10.1002/mrm.26623] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Revised: 11/21/2016] [Accepted: 01/08/2017] [Indexed: 12/30/2022]
Abstract
PURPOSE In diffusion MRI (dMRI), fractional anisotropy derived from the single-tensor model (FADTI ) is the most widely used metric to characterize white matter (WM) microarchitecture, despite known limitations in regions with crossing fibers. Due to time constraints when scanning patients in clinical settings, high angular resolution diffusion imaging acquisition protocols, often used to overcome these limitations, are still rare in clinical population studies. However, the tensor distribution function (TDF) may be used to model multiple underlying fibers by representing the diffusion profile as a probabilistic mixture of tensors. METHODS We compared the ability of standard FADTI and TDF-derived FA (FATDF ), calculated from a range of dMRI angular resolutions (41, 30, 15, and 7 gradient directions), to profile WM deficits in 251 individuals from the Alzheimer's Disease Neuroimaging Initiative and to detect associations with 1) Alzheimer's disease diagnosis, 2) Clinical Dementia Rating scores, and 3) average hippocampal volume. RESULTS Across angular resolutions and statistical tests, FATDF showed larger effect sizes than FADTI , particularly in regions preferentially affected by Alzheimer's disease, and was less susceptible to crossing fiber anomalies. CONCLUSION The TDF "corrected" form of FA may be a more sensitive and accurate alternative to the commonly used FADTI , even in clinical quality dMRI data. Magn Reson Med 78:2322-2333, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
Collapse
Affiliation(s)
- Talia M Nir
- Imaging Genetics Center, University of Southern California, Marina del Rey, California, USA
| | - Neda Jahanshad
- Imaging Genetics Center, University of Southern California, Marina del Rey, California, USA
| | - Julio E Villalon-Reina
- Imaging Genetics Center, University of Southern California, Marina del Rey, California, USA
| | - Dmitry Isaev
- Imaging Genetics Center, University of Southern California, Marina del Rey, California, USA
| | | | - Liang Zhan
- Computer Engineering Program, University of Wisconsin-Stout, Menomonie, Wisconsin, USA
| | - Alex D Leow
- Department of Psychiatry and Bioengineering, University of Illinois, Chicago, Illinois, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic and Foundation, Rochester, Minnesota, USA
| | - Michael W Weiner
- Department of Radiology, University of California San Francisco School of Medicine, San Francisco, California, USA
| | - Paul M Thompson
- Imaging Genetics Center, University of Southern California, Marina del Rey, California, USA
| | | |
Collapse
|
19
|
Jenkins LM, Barba A, Campbell M, Lamar M, Shankman SA, Leow AD, Ajilore O, Langenecker SA. Shared white matter alterations across emotional disorders: A voxel-based meta-analysis of fractional anisotropy. Neuroimage Clin 2016; 12:1022-1034. [PMID: 27995068 PMCID: PMC5153602 DOI: 10.1016/j.nicl.2016.09.001] [Citation(s) in RCA: 102] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2016] [Revised: 08/30/2016] [Accepted: 09/01/2016] [Indexed: 02/02/2023]
Abstract
Background White matter (WM) integrity may represent a shared biomarker for emotional disorders (ED). Aims: To identify transdiagnostic biomarkers of reduced WM by meta-analysis of findings across multiple EDs. Method Web of Science was searched systematically for studies of whole brain analysis of fractional anisotropy (FA) in adults with major depressive disorder, bipolar disorder, social anxiety disorder, obsessive-compulsive disorder or posttraumatic stress disorder compared with a healthy control (HC) group. Peak MNI coordinates were extracted from 37 studies of voxel-based analysis (892 HC and 962 with ED) and meta-analyzed using seed-based d Mapping (SDM) Version 4.31. Separate meta-analyses were also conducted for each disorder. Results In the transdiagnostic meta-analysis, reduced FA was identified in ED studies compared to HCs in the left inferior fronto-occipital fasciculus, forceps minor, uncinate fasciculus, anterior thalamic radiation, superior corona radiata, bilateral superior longitudinal fasciculi, and cerebellum. Disorder-specific meta-analyses revealed the OCD group had the most similarities in reduced FA to other EDs, with every cluster of reduced FA overlapping with at least one other diagnosis. The PTSD group was the most distinct, with no clusters of reduced FA overlapping with any other diagnosis. The BD group were the only disorder to show increased FA in any region, and showed a more bilateral pattern of WM changes, compared to the other groups which tended to demonstrate a left lateralized pattern of FA reductions. Conclusions Distinct diagnostic categories of ED show commonalities in WM tracts with reduced FA when compared to HC, which links brain networks involved in cognitive and affective processing. This meta-analysis facilitates an increased understanding of the biological markers that are shared by these ED. A meta-analysis of FA in MDD, bipolar, social anxiety disorder, OCD and PTSD Reduced FA in left superior longitudinal and inferior fronto-occipital fasciculi Distinct diagnostic categories show commonalities of white matter changes. Differences among diagnostic categories also found, PTSD most distinct White matter integrity may be a shared biomarker for emotional disorders.
Collapse
Affiliation(s)
| | - Alyssa Barba
- The University of Illinois at Chicago, Department of Psychiatry
| | | | - Melissa Lamar
- The University of Illinois at Chicago, Department of Psychiatry
| | | | - Alex D Leow
- The University of Illinois at Chicago, Department of Psychiatry
| | - Olusola Ajilore
- The University of Illinois at Chicago, Department of Psychiatry
| | | |
Collapse
|
20
|
Hua X, Ching CRK, Mezher A, Gutman BA, Hibar DP, Bhatt P, Leow AD, Jack CR, Bernstein MA, Weiner MW, Thompson PM. MRI-based brain atrophy rates in ADNI phase 2: acceleration and enrichment considerations for clinical trials. Neurobiol Aging 2015; 37:26-37. [PMID: 26545631 PMCID: PMC4827255 DOI: 10.1016/j.neurobiolaging.2015.09.018] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2015] [Revised: 08/30/2015] [Accepted: 09/22/2015] [Indexed: 01/31/2023]
Abstract
The goal of this work was to assess statistical power to detect treatment effects in Alzheimer’s disease (AD) clinical trials using magnetic resonance imaging (MRI)–derived brain biomarkers. We used unbiased tensor-based morphometry (TBM) to analyze n = 5,738 scans, from Alzheimer’s Disease Neuroimaging Initiative 2 participants scanned with both accelerated and nonaccelerated T1-weighted MRI at 3T. The study cohort included 198 healthy controls, 111 participants with significant memory complaint, 182 with early mild cognitive impairment (EMCI) and 177 late mild cognitive impairment (LMCI), and 155 AD patients, scanned at screening and 3, 6, 12, and 24 months. The statistical power to track brain change in TBM-based imaging biomarkers depends on the interscan interval, disease stage, and methods used to extract numerical summaries. To achieve reasonable sample size estimates for potential clinical trials, the minimal scan interval was 6 months for LMCI and AD and 12 months for EMCI. TBM-based imaging biomarkers were not sensitive to MRI scan acceleration, which gave results comparable with nonaccelerated sequences. ApoE status and baseline amyloid-beta positron emission tomography data improved statistical power. Among healthy, EMCI, and LMCI participants, sample size requirements were significantly lower in the amyloid+/ApoE4+ group than for the amyloid−/ApoE4− group. ApoE4 strongly predicted atrophy rates across brain regions most affected by AD, but the remaining 9 of the top 10 AD risk genes offered no added predictive value in this cohort.
Collapse
Affiliation(s)
- Xue Hua
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA, USA; Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Christopher R K Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA, USA; Interdepartmental Neuroscience Graduate Program, University of California, Los Angeles, School of Medicine, Los Angeles, CA, USA
| | - Adam Mezher
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Boris A Gutman
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Derrek P Hibar
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA, USA; Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Priya Bhatt
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Alex D Leow
- Department of Psychiatry, University of Illinois at Chicago, College of Medicine, Chicago, IL, USA; Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA
| | | | | | - Michael W Weiner
- Department of Radiology, University of California, San Francisco, San Francisco, CA, USA; Department of Medicine and Psychiatry, University of California, San Francisco, San Francisco, CA, USA; Department Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA, USA; Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Psychiatry, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Engineering, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Ophthalmology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | | |
Collapse
|
21
|
Lee GJ, Lu PH, Mather MJ, Shapira J, Jimenez E, Leow AD, Thompson PM, Mendez MF. Neuroanatomical correlates of emotional blunting in behavioral variant frontotemporal dementia and early-onset Alzheimer's disease. J Alzheimers Dis 2015; 41:793-800. [PMID: 24685626 DOI: 10.3233/jad-132219] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND Emotional blunting is a characteristic feature of behavioral variant frontotemporal dementia (bvFTD) and can help discriminate between patients with bvFTD and other forms of younger-onset dementia. OBJECTIVE We compared the presence of emotional blunting symptoms in patients with bvFTD and early-onset Alzheimer's disease (AD), and investigated the neuroanatomical associations between emotional blunting and regional brain volume. METHODS Twenty-five individuals with bvFTD (n = 11) and early-onset AD (n = 14) underwent magnetic resonance imaging (MRI) and were rated on symptoms of emotional blunting using the Scale for Emotional Blunting (SEB). The two groups were compared on SEB ratings and MRI-derived brain volume using tensor-based morphometry. Voxel-wise linear regression was performed to determine neuroanatomical correlates of SEB scores. RESULTS The bvFTD group had significantly higher SEB scores compared to the AD group. On MRI, bvFTD patients had smaller bilateral frontal lobe volume compared to AD patients, while AD patients had smaller bilateral temporal and left parietal volume than bvFTD patients. In bvFTD, SEB ratings were strongly correlated with right anterior temporal volume, while the association between SEB and the right orbitofrontal cortex was non-significant. CONCLUSIONS Symptoms of emotional blunting were more prevalent in bvFTD than early-onset AD patients. These symptoms were particularly associated with right-sided atrophy, with significant involvement of the right anterior temporal region. Based on these findings, the SEB appears to measure symptoms of emotional blunting that are localized to the right anterior temporal lobe.
Collapse
Affiliation(s)
- Grace J Lee
- Department of Psychology, School of Behavioral Health, Loma Linda University, Loma Linda, CA, USA
| | - Po H Lu
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Michelle J Mather
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA Greater Los Angeles VA Healthcare System, West Los Angeles, CA, USA
| | - Jill Shapira
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA Greater Los Angeles VA Healthcare System, West Los Angeles, CA, USA
| | - Elvira Jimenez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA Greater Los Angeles VA Healthcare System, West Los Angeles, CA, USA
| | - Alex D Leow
- Departments of Psychiatry and Bioengineering, University of Illinois, Chicago, IL, USA
| | - Paul M Thompson
- Laboratory of NeuroImaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Mario F Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA Greater Los Angeles VA Healthcare System, West Los Angeles, CA, USA
| |
Collapse
|
22
|
Torgerson CM, Irimia A, Leow AD, Bartzokis G, Moody TD, Jennings RG, Alger JR, Van Horn JD, Altshuler LL. DTI tractography and white matter fiber tract characteristics in euthymic bipolar I patients and healthy control subjects. Brain Imaging Behav 2013; 7:129-39. [PMID: 23070746 DOI: 10.1007/s11682-012-9202-3] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
With the introduction of diffusion tensor imaging (DTI), structural differences in white matter (WM) architecture between psychiatric populations and healthy controls can be systematically observed and measured. In particular, DTI-tractography can be used to assess WM characteristics over the entire extent of WM tracts and aggregated fiber bundles. Using 64-direction DTI scanning in 27 participants with bipolar disorder (BD) and 26 age-and-gender-matched healthy control subjects, we compared relative length, density, and fractional anisotrophy (FA) of WM tracts involved in emotion regulation or theorized to be important neural components in BD neuropathology. We interactively isolated 22 known white matter tracts using region-of-interest placement (TrackVis software program) and then computed relative tract length, density, and integrity. BD subjects demonstrated significantly shorter WM tracts in the genu, body and splenium of the corpus callosum compared to healthy controls. Additionally, bipolar subjects exhibited reduced fiber density in the genu and body of the corpus callosum, and in the inferior longitudinal fasciculus bilaterally. In the left uncinate fasciculus, however, BD subjects exhibited significantly greater fiber density than healthy controls. There were no significant differences between groups in WM tract FA for those tracts that began and ended in the brain. The significance of differences in tract length and fiber density in BD is discussed.
Collapse
Affiliation(s)
- Carinna M Torgerson
- Laboratory of Neuro Imaging LONI, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, 635 Charles E. Young Dr. S, Los Angeles, CA 90095, USA.
| | | | | | | | | | | | | | | | | |
Collapse
|
23
|
Lu PH, Mendez MF, Lee GJ, Leow AD, Lee HW, Shapira J, Jimenez E, Boeve BB, Caselli RJ, Graff-Radford NR, Jack CR, Kramer JH, Miller BL, Bartzokis G, Thompson PM, Knopman DS. Patterns of brain atrophy in clinical variants of frontotemporal lobar degeneration. Dement Geriatr Cogn Disord 2013; 35:34-50. [PMID: 23306166 PMCID: PMC3609420 DOI: 10.1159/000345523] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/31/2012] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND/AIMS The clinical syndromes of frontotemporal lobar degeneration include behavioral variant frontotemporal dementia (bvFTD) and semantic (SV-PPA) and nonfluent variants (NF-PPA) of primary progressive aphasia. Using magnetic resonance imaging (MRI), tensor-based morphometry (TBM) was used to determine distinct patterns of atrophy between these three clinical groups. METHODS Twenty-seven participants diagnosed with bvFTD, 16 with SV-PPA, and 19 with NF-PPA received baseline and follow-up MRI scans approximately 1 year apart. TBM was used to create three-dimensional Jacobian maps of local brain atrophy rates for individual subjects. RESULTS Regional analyses were performed on the three-dimensional maps and direct comparisons between groups (corrected for multiple comparisons using permutation tests) revealed significantly greater frontal lobe and frontal white matter atrophy in the bvFTD relative to the SV-PPA group (p < 0.005). The SV-PPA subjects exhibited significantly greater atrophy than the bvFTD in the fusiform gyrus (p = 0.007). The NF-PPA group showed significantly more atrophy in the parietal lobes relative to both bvFTD and SV-PPA groups (p < 0.05). Percent volume change in ventromedial prefrontal cortex was significantly associated with baseline behavioral symptomatology. CONCLUSION The bvFTD, SV-PPA, and NF-PPA groups displayed distinct patterns of progressive atrophy over a 1-year period that correspond well to the behavioral disturbances characteristic of the clinical syndromes. More specifically, the bvFTD group showed significant white matter contraction and presence of behavioral symptoms at baseline predicted significant volume loss of the ventromedial prefrontal cortex.
Collapse
Affiliation(s)
- Po H Lu
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
24
|
Gadelkarim JJ, Ajilore O, Schonfeld D, Zhan L, Thompson PM, Feusner JD, Kumar A, Altshuler LL, Leow AD. Investigating brain community structure abnormalities in bipolar disorder using path length associated community estimation. Hum Brain Mapp 2013; 35:2253-64. [PMID: 23798337 DOI: 10.1002/hbm.22324] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2012] [Revised: 03/11/2013] [Accepted: 04/18/2013] [Indexed: 12/15/2022] Open
Abstract
In this article, we present path length associated community estimation (PLACE), a comprehensive framework for studying node-level community structure. Instead of the well-known Q modularity metric, PLACE utilizes a novel metric, Ψ(PL), which measures the difference between intercommunity versus intracommunity path lengths. We compared community structures in human healthy brain networks generated using these two metrics and argued that Ψ(PL) may have theoretical advantages. PLACE consists of the following: (1) extracting community structure using top-down hierarchical binary trees, where a branch at each bifurcation denotes a collection of nodes that form a community at that level, (2) constructing and assessing mean group community structure, and (3) detecting node-level changes in community between groups. We applied PLACE and investigated the structural brain networks obtained from a sample of 25 euthymic bipolar I subjects versus 25 gender- and age-matched healthy controls. Results showed community structural differences in posterior default mode network regions, with the bipolar group exhibiting left-right decoupling.
Collapse
Affiliation(s)
- Johnson J Gadelkarim
- Electrical and Computer Engineering department, University of Illinois at Chicago, Chicago, Illinois; Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois
| | | | | | | | | | | | | | | | | |
Collapse
|
25
|
Lee GJ, Lu PH, Medina LD, Rodriguez-Agudelo Y, Melchor S, Coppola G, Braskie MN, Hua X, Apostolova LG, Leow AD, Thompson PM, Ringman JM. Regional brain volume differences in symptomatic and presymptomatic carriers of familial Alzheimer's disease mutations. J Neurol Neurosurg Psychiatry 2013; 84:154-62. [PMID: 23085935 PMCID: PMC3779052 DOI: 10.1136/jnnp-2011-302087] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
BACKGROUND Mutations in the presenilin (PSEN1, PSEN2) and amyloid precursor protein (APP) genes cause familial Alzheimer's disease (FAD) in a nearly fully penetrant, autosomal dominant manner, providing a unique opportunity to study presymptomatic individuals who can be predicted to develop Alzheimer's disease (AD) with essentially 100% certainty. Using tensor-based morphometry (TBM), we examined brain volume differences between presymptomatic and symptomatic FAD mutation carriers and non-carrier (NC) relatives. METHODS Twenty-five mutation carriers and 10 NC relatives underwent brain MRI and clinical assessment. Four mutation carriers had dementia (MUT-Dem), 12 had amnestic mild cognitive impairment (MUT-aMCI) and nine were cognitively normal (MUT-Norm). TBM brain volume maps of MUT-Norm, MUT-aMCI and MUT-Dem subjects were compared to NC subjects. RESULTS MUT-Norm subjects exhibited significantly smaller volumes in the thalamus, caudate and putamen. MUT-aMCI subjects had smaller volumes in the thalamus, splenium and pons, but not in the caudate or putamen. MUT-Dem subjects demonstrated smaller volumes in temporal, parietal and left frontal regions. As non-demented carriers approached the expected age of dementia diagnosis, this was associated with larger ventricular and caudate volumes and a trend towards smaller temporal lobe volume. CONCLUSIONS Cognitively intact FAD mutation carriers had lower thalamic, caudate and putamen volumes, and we found preliminary evidence for increasing caudate size during the predementia stage. These regions may be affected earliest during prodromal stages of FAD, while cortical atrophy may occur in later stages, when carriers show cognitive deficits. Further studies of this population will help us understand the progression of neurobiological changes in AD.
Collapse
Affiliation(s)
- Grace J Lee
- Mary S. Easton Center for Alzheimer's Disease Research, Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095-7226, USA.
| | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
26
|
Hua X, Thompson PM, Leow AD, Madsen SK, Caplan R, Alger JR, O'Neill J, Joshi K, Smalley SL, Toga AW, Levitt JG. Brain growth rate abnormalities visualized in adolescents with autism. Hum Brain Mapp 2013; 34:425-36. [PMID: 22021093 PMCID: PMC4144412 DOI: 10.1002/hbm.21441] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2010] [Accepted: 07/27/2011] [Indexed: 11/10/2022] Open
Abstract
Autism spectrum disorder is a heterogeneous disorder of brain development with wide ranging cognitive deficits. Typically diagnosed before age 3, autism spectrum disorder is behaviorally defined but patients are thought to have protracted alterations in brain maturation. With longitudinal magnetic resonance imaging (MRI), we mapped an anomalous developmental trajectory of the brains of autistic compared with those of typically developing children and adolescents. Using tensor-based morphometry, we created 3D maps visualizing regional tissue growth rates based on longitudinal brain MRI scans of 13 autistic and seven typically developing boys (mean age/interscan interval: autism 12.0 ± 2.3 years/2.9 ± 0.9 years; control 12.3 ± 2.4/2.8 ± 0.8). The typically developing boys demonstrated strong whole brain white matter growth during this period, but the autistic boys showed abnormally slowed white matter development (P = 0.03, corrected), especially in the parietal (P = 0.008), temporal (P = 0.03), and occipital lobes (P = 0.02). We also visualized abnormal overgrowth in autism in gray matter structures such as the putamen and anterior cingulate cortex. Our findings reveal aberrant growth rates in brain regions implicated in social impairment, communication deficits and repetitive behaviors in autism, suggesting that growth rate abnormalities persist into adolescence. Tensor-based morphometry revealed persisting growth rate anomalies long after diagnosis, which has implications for evaluation of therapeutic effects.
Collapse
Affiliation(s)
- Xue Hua
- Laboratory of Neuro Imaging, University of California Los Angeles School of Medicine, Los Angeles, California
| | - Paul M. Thompson
- Laboratory of Neuro Imaging, University of California Los Angeles School of Medicine, Los Angeles, California
| | - Alex D. Leow
- Laboratory of Neuro Imaging, University of California Los Angeles School of Medicine, Los Angeles, California
- Semel Institute of Neuroscience, University of California Los Angeles School of Medicine, Los Angeles, California
| | - Sarah K. Madsen
- Laboratory of Neuro Imaging, University of California Los Angeles School of Medicine, Los Angeles, California
| | - Rochelle Caplan
- Department of Psychiatry and Biobehavioral Sciences, Division of Child Psychiatry, University of California Los Angeles School of Medicine, Los Angeles, California
| | - Jeffry R. Alger
- Ahmanson‐Lovelace Brain Mapping Center, Department of Neurology, University of California Los Angeles School of Medicine, Los Angeles, California
| | - Joseph O'Neill
- Department of Psychiatry and Biobehavioral Sciences, Division of Child Psychiatry, University of California Los Angeles School of Medicine, Los Angeles, California
| | - Kishori Joshi
- Department of Psychiatry and Biobehavioral Sciences, Division of Child Psychiatry, University of California Los Angeles School of Medicine, Los Angeles, California
| | - Susan L. Smalley
- Department of Psychiatry and Biobehavioral Sciences, Division of Child Psychiatry, University of California Los Angeles School of Medicine, Los Angeles, California
| | - Arthur W. Toga
- Laboratory of Neuro Imaging, University of California Los Angeles School of Medicine, Los Angeles, California
| | - Jennifer G. Levitt
- Department of Psychiatry and Biobehavioral Sciences, Division of Child Psychiatry, University of California Los Angeles School of Medicine, Los Angeles, California
| |
Collapse
|
27
|
Kim WH, Adluru N, Chung MK, Charchut S, GadElkarim JJ, Altshuler L, Moody T, Kumar A, Singh V, Leow AD. Multi-resolutional brain network filtering and analysis via wavelets on non-Euclidean space. Med Image Comput Comput Assist Interv 2013; 16:643-51. [PMID: 24505816 DOI: 10.1007/978-3-642-40760-4_80] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Advances in resting state fMRI and diffusion weighted imaging (DWI) have led to much interest in studies that evaluate hypotheses focused on how brain connectivity networks show variations across clinically disparate groups. However, various sources of error (e.g., tractography errors, magnetic field distortion, and motion artifacts) leak into the data, and make downstream statistical analysis problematic. In small sample size studies, such noise have an unfortunate effect that the differential signal may not be identifiable and so the null hypothesis cannot be rejected. Traditionally, smoothing is often used to filter out noise. But the construction of convolving with a Gaussian kernel is not well understood on arbitrarily connected graphs. Furthermore, there are no direct analogues of scale-space theory for graphs--ones which allow to view the signal at multiple resolutions. We provide rigorous frameworks for performing 'multi-resolutional' analysis on brain connectivity graphs. These are based on the recent theory of non-Euclidean wavelets. We provide strong evidence, on brain connectivity data from a network analysis study (structural connectivity differences in adult euthymic bipolar subjects), that the proposed algorithm allows identifying statistically significant network variations, which are clinically meaningful, where classical statistical tests, if applied directly, fail.
Collapse
|
28
|
Hua X, Hibar DP, Ching CRK, Boyle CP, Rajagopalan P, Gutman BA, Leow AD, Toga AW, Jack CR, Harvey D, Weiner MW, Thompson PM. Unbiased tensor-based morphometry: improved robustness and sample size estimates for Alzheimer's disease clinical trials. Neuroimage 2012; 66:648-61. [PMID: 23153970 DOI: 10.1016/j.neuroimage.2012.10.086] [Citation(s) in RCA: 88] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2012] [Revised: 10/29/2012] [Accepted: 10/30/2012] [Indexed: 01/11/2023] Open
Abstract
Various neuroimaging measures are being evaluated for tracking Alzheimer's disease (AD) progression in therapeutic trials, including measures of structural brain change based on repeated scanning of patients with magnetic resonance imaging (MRI). Methods to compute brain change must be robust to scan quality. Biases may arise if any scans are thrown out, as this can lead to the true changes being overestimated or underestimated. Here we analyzed the full MRI dataset from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) and assessed several sources of bias that can arise when tracking brain changes with structural brain imaging methods, as part of a pipeline for tensor-based morphometry (TBM). In all healthy subjects who completed MRI scanning at screening, 6, 12, and 24months, brain atrophy was essentially linear with no detectable bias in longitudinal measures. In power analyses for clinical trials based on these change measures, only 39AD patients and 95 mild cognitive impairment (MCI) subjects were needed for a 24-month trial to detect a 25% reduction in the average rate of change using a two-sided test (α=0.05, power=80%). Further sample size reductions were achieved by stratifying the data into Apolipoprotein E (ApoE) ε4 carriers versus non-carriers. We show how selective data exclusion affects sample size estimates, motivating an objective comparison of different analysis techniques based on statistical power and robustness. TBM is an unbiased, robust, high-throughput imaging surrogate marker for large, multi-site neuroimaging studies and clinical trials of AD and MCI.
Collapse
Affiliation(s)
- Xue Hua
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA 90095-1769, USA
| | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
29
|
Lee GJ, Lu PH, Hua X, Lee S, Wu S, Nguyen K, Teng E, Leow AD, Jack CR, Toga AW, Weiner MW, Bartzokis G, Thompson PM. Depressive symptoms in mild cognitive impairment predict greater atrophy in Alzheimer's disease-related regions. Biol Psychiatry 2012; 71:814-21. [PMID: 22322105 PMCID: PMC3322258 DOI: 10.1016/j.biopsych.2011.12.024] [Citation(s) in RCA: 107] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2011] [Revised: 11/18/2011] [Accepted: 12/06/2011] [Indexed: 01/20/2023]
Abstract
BACKGROUND Depression has been associated with higher conversion rates from mild cognitive impairment (MCI) to Alzheimer's disease (AD) and may be a marker of prodromal AD that can be used to identify individuals with MCI who are most likely to progress to AD. Thus, we examined the neuroanatomical changes associated with depressive symptoms in MCI. METHODS Two-hundred forty-three MCI subjects from the Alzheimer's Disease Neuroimaging Initiative who had brain magnetic resonance imaging scans at baseline and 2-year follow-up were classified into depressed (n = 44), nondepressed with other neuropsychiatric symptoms (n = 93), and no-symptom (NOSYMP; n = 106) groups based on the Neuropsychiatric Inventory Questionnaire. Tensor-based morphometry was used to create individual three-dimensional maps of 2-year brain changes that were compared between groups. RESULTS Depressed subjects had more frontal (p = .024), parietal (p = .030), and temporal (p = .038) white matter atrophy than NOSYMP subjects. Those whose depressive symptoms persisted over 2 years also had higher conversion to AD and more decline on measures of global cognition, language, and executive functioning compared with stable NOSYMP subjects. Nondepressed with other neuropsychiatric symptoms and NOSYMP groups exhibited no differences in rates of atrophy. CONCLUSIONS Depressive symptoms were associated with greater atrophy in AD-affected regions, increased cognitive decline, and higher rates of conversion to AD. Depression in individuals with MCI may be associated with underlying neuropathological changes, including prodromal AD, and may be a potentially useful clinical marker in identifying MCI patients who are most likely to progress to AD.
Collapse
Affiliation(s)
- Grace J Lee
- Department of Neurology, David Geffen School of Medicine at University of California Los Angeles, USA.
| | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
30
|
Zhan L, Jahanshad N, Ennis DB, Jin Y, Bernstein MA, Borowski BJ, Jack CR, Toga AW, Leow AD, Thompson PM. Angular versus spatial resolution trade-offs for diffusion imaging under time constraints. Hum Brain Mapp 2012; 34:2688-706. [PMID: 22522814 DOI: 10.1002/hbm.22094] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2012] [Accepted: 03/15/2012] [Indexed: 12/14/2022] Open
Abstract
Diffusion weighted magnetic resonance imaging (DW-MRI) are now widely used to assess brain integrity in clinical populations. The growing interest in mapping brain connectivity has made it vital to consider what scanning parameters affect the accuracy, stability, and signal-to-noise of diffusion measures. Trade-offs between scan parameters can only be optimized if their effects on various commonly-derived measures are better understood. To explore angular versus spatial resolution trade-offs in standard tensor-derived measures, and in measures that use the full angular information in diffusion signal, we scanned eight subjects twice, 2 weeks apart, using three protocols that took the same amount of time (7 min). Scans with 3.0, 2.7, 2.5 mm isotropic voxels were collected using 48, 41, and 37 diffusion-sensitized gradients to equalize scan times. A specially designed DTI phantom was also scanned with the same protocols, and different b-values. We assessed how several diffusion measures including fractional anisotropy (FA), mean diffusivity (MD), and the full 3D orientation distribution function (ODF) depended on the spatial/angular resolution and the SNR. We also created maps of stability over time in the FA, MD, ODF, skeleton FA of 14 TBSS-derived ROIs, and an information uncertainty index derived from the tensor distribution function, which models the signal using a continuous mixture of tensors. In scans of the same duration, higher angular resolution and larger voxels boosted SNR and improved stability over time. The increased partial voluming in large voxels also led to bias in estimating FA, but this was partially addressed by using "beyond-tensor" models of diffusion.
Collapse
Affiliation(s)
- Liang Zhan
- Department of Neurology, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, California
| | | | | | | | | | | | | | | | | | | |
Collapse
|
31
|
Leow AD, Zhan L, Arienzo D, GadElkarim JJ, Zhang AF, Ajilore O, Kumar A, Thompson PM, Feusner JD. Hierarchical structural mapping for globally optimized estimation of functional networks. Med Image Comput Comput Assist Interv 2012; 15:228-36. [PMID: 23286053 DOI: 10.1007/978-3-642-33418-4_29] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
In this study, we propose a framework to map functional MRI (fMRI) activation signals using DTI-tractography. This framework, which we term functional by structural hierarchical (FSH) mapping, models the regional origin of fMRI brain activation to construct "N-step reachable structural maps". Linear combinations of these N-step reachable maps are then used to predict the observed fMRI signals. Additionally, we constructed a utilization matrix, which numerically estimates whether the inclusion of a specific structural connection better predicts fMRI, using simulated annealing. We applied this framework to a visual fMRI task in a sample of body dysmorphic disorder (BDD) subjects and comparable healthy controls. Group differences were inferred by comparing the observed utilization differences against 10,000 permutations under the null hypothesis. Results revealed that BDD subjects under-utilized several key local connections in the visual system, which may help explain previously reported fMRI findings and further elucidate the underlying pathophysiology of BDD.
Collapse
Affiliation(s)
- Alex D Leow
- Department of Psychiatry, University of Illinois, Chicago, IL, USA.
| | | | | | | | | | | | | | | | | |
Collapse
|
32
|
Ho AJ, Hua X, Lee S, Leow AD, Yanovsky I, Gutman B, Dinov ID, Leporé N, Stein JL, Toga AW, Jack CR, Bernstein MA, Reiman EM, Harvey DJ, Kornak J, Schuff N, Alexander GE, Weiner MW, Thompson PM. Comparing 3 T and 1.5 T MRI for tracking Alzheimer's disease progression with tensor-based morphometry. Hum Brain Mapp 2010; 31:499-514. [PMID: 19780044 DOI: 10.1002/hbm.20882] [Citation(s) in RCA: 64] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
A key question in designing MRI-based clinical trials is how the main magnetic field strength of the scanner affects the power to detect disease effects. In 110 subjects scanned longitudinally at both 3.0 and 1.5 T, including 24 patients with Alzheimer's Disease (AD) [74.8 +/- 9.2 years, MMSE: 22.6 +/- 2.0 at baseline], 51 individuals with mild cognitive impairment (MCI) [74.1 +/- 8.0 years, MMSE: 26.6 +/- 2.0], and 35 controls [75.9 +/- 4.6 years, MMSE: 29.3 +/- 0.8], we assessed whether higher-field MR imaging offers higher or lower power to detect longitudinal changes in the brain, using tensor-based morphometry (TBM) to reveal the location of progressive atrophy. As expected, at both field strengths, progressive atrophy was widespread in AD and more spatially restricted in MCI. Power analysis revealed that, to detect a 25% slowing of atrophy (with 80% power), 37 AD and 108 MCI subjects would be needed at 1.5 T versus 49 AD and 166 MCI subjects at 3 T; however, the increased power at 1.5 T was not statistically significant (alpha = 0.05) either for TBM, or for SIENA, a related method for computing volume loss rates. Analysis of cumulative distribution functions and false discovery rates showed that, at both field strengths, temporal lobe atrophy rates were correlated with interval decline in Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-cog), mini-mental status exam (MMSE), and Clinical Dementia Rating sum-of-boxes (CDR-SB) scores. Overall, 1.5 and 3 T scans did not significantly differ in their power to detect neurodegenerative changes over a year. Hum Brain Mapp, 2010. (c) 2009 Wiley-Liss, Inc.
Collapse
Affiliation(s)
- April J Ho
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, California 90095-1769., USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
33
|
Hua X, Lee S, Hibar DP, Yanovsky I, Leow AD, Toga AW, Jack CR, Bernstein MA, Reiman EM, Harvey DJ, Kornak J, Schuff N, Alexander GE, Weiner MW, Thompson PM. Mapping Alzheimer's disease progression in 1309 MRI scans: power estimates for different inter-scan intervals. Neuroimage 2010; 51:63-75. [PMID: 20139010 PMCID: PMC2846999 DOI: 10.1016/j.neuroimage.2010.01.104] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2009] [Revised: 01/26/2010] [Accepted: 01/29/2010] [Indexed: 12/31/2022] Open
Abstract
Neuroimaging centers and pharmaceutical companies are working together to evaluate treatments that might slow the progression of Alzheimer's disease (AD), a common but devastating late-life neuropathology. Recently, automated brain mapping methods, such as tensor-based morphometry (TBM) of structural MRI, have outperformed cognitive measures in their precision and power to track disease progression, greatly reducing sample size estimates for drug trials. In the largest TBM study to date, we studied how sample size estimates for tracking structural brain changes depend on the time interval between the scans (6-24 months). We analyzed 1309 brain scans from 91 probable AD patients (age at baseline: 75.4+/-7.5 years) and 189 individuals with mild cognitive impairment (MCI; 74.6+/-7.1 years), scanned at baseline, 6, 12, 18, and 24 months. Statistical maps revealed 3D patterns of brain atrophy at each follow-up scan relative to the baseline; numerical summaries were used to quantify temporal lobe atrophy within a statistically-defined region-of-interest. Power analyses revealed superior sample size estimates over traditional clinical measures. Only 80, 46, and 39 AD patients were required for a hypothetical clinical trial, at 6, 12, and 24 months respectively, to detect a 25% reduction in average change using a two-sided test (alpha=0.05, power=80%). Correspondingly, 106, 79, and 67 subjects were needed for an equivalent MCI trial aiming for earlier intervention. A 24-month trial provides most power, except when patient attrition exceeds 15-16%/year, in which case a 12-month trial is optimal. These statistics may facilitate clinical trial design using voxel-based brain mapping methods such as TBM.
Collapse
Affiliation(s)
- Xue Hua
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | - Suh Lee
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | - Derrek P. Hibar
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | - Igor Yanovsky
- Department of Mathematics, UCLA, Los Angeles, CA, USA
| | - Alex D. Leow
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
- Resnick Neuropsychiatric Hospital at UCLA, Los Angeles, CA, USA
| | - Arthur W. Toga
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | | | | | - Eric M. Reiman
- Banner Alzheimer’s Institute, Department Psychiatry, University of Arizona, Phoenix, AZ, USA
| | - Danielle J. Harvey
- Department of Public Health Sciences, UCD School of Medicine, Davis, CA, USA
| | - John Kornak
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, UCSF, San Francisco, CA, USA
| | - Norbert Schuff
- Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
| | - Gene E. Alexander
- Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
| | - Michael W. Weiner
- Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
- Department of Medicine, UCSF, San Francisco, CA, USA
- Department of Psychiatry, UCSF, San Francisco, CA, USA
| | - Paul M. Thompson
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | | |
Collapse
|
34
|
Stein JL, Hua X, Morra JH, Lee S, Hibar DP, Ho AJ, Leow AD, Toga AW, Sul JH, Kang HM, Eskin E, Saykin AJ, Shen L, Foroud T, Pankratz N, Huentelman MJ, Craig DW, Gerber JD, Allen AN, Corneveaux JJ, Stephan DA, Webster J, DeChairo BM, Potkin SG, Jack CR, Weiner MW, Thompson PM. Genome-wide analysis reveals novel genes influencing temporal lobe structure with relevance to neurodegeneration in Alzheimer's disease. Neuroimage 2010; 51:542-54. [PMID: 20197096 DOI: 10.1016/j.neuroimage.2010.02.068] [Citation(s) in RCA: 103] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2009] [Revised: 01/15/2010] [Accepted: 02/22/2010] [Indexed: 12/16/2022] Open
Abstract
In a genome-wide association study of structural brain degeneration, we mapped the 3D profile of temporal lobe volume differences in 742 brain MRI scans of Alzheimer's disease patients, mildly impaired, and healthy elderly subjects. After searching 546,314 genomic markers, 2 single nucleotide polymorphisms (SNPs) were associated with bilateral temporal lobe volume (P<5 x 10(-7)). One SNP, rs10845840, is located in the GRIN2B gene which encodes the N-methyl-d-aspartate (NMDA) glutamate receptor NR2B subunit. This protein - involved in learning and memory, and excitotoxic cell death - has age-dependent prevalence in the synapse and is already a therapeutic target in Alzheimer's disease. Risk alleles for lower temporal lobe volume at this SNP were significantly over-represented in AD and MCI subjects vs. controls (odds ratio=1.273; P=0.039) and were associated with mini-mental state exam scores (MMSE; t=-2.114; P=0.035) demonstrating a negative effect on global cognitive function. Voxelwise maps of genetic association of this SNP with regional brain volumes, revealed intense temporal lobe effects (FDR correction at q=0.05; critical P=0.0257). This study uses large-scale brain mapping for gene discovery with implications for Alzheimer's disease.
Collapse
Affiliation(s)
- Jason L Stein
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
35
|
Raji CA, Ho AJ, Parikshak NN, Becker JT, Lopez OL, Kuller LH, Hua X, Leow AD, Toga AW, Thompson PM. Brain structure and obesity. Hum Brain Mapp 2010; 31:353-64. [PMID: 19662657 PMCID: PMC2826530 DOI: 10.1002/hbm.20870] [Citation(s) in RCA: 323] [Impact Index Per Article: 23.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2009] [Revised: 06/03/2009] [Accepted: 07/03/2009] [Indexed: 11/06/2022] Open
Abstract
Obesity is associated with increased risk for cardiovascular health problems including diabetes, hypertension, and stroke. These cardiovascular afflictions increase risk for cognitive decline and dementia, but it is unknown whether these factors, specifically obesity and Type II diabetes, are associated with specific patterns of brain atrophy. We used tensor-based morphometry (TBM) to examine gray matter (GM) and white matter (WM) volume differences in 94 elderly subjects who remained cognitively normal for at least 5 years after their scan. Bivariate analyses with corrections for multiple comparisons strongly linked body mass index (BMI), fasting plasma insulin (FPI) levels, and Type II Diabetes Mellitus (DM2) with atrophy in frontal, temporal, and subcortical brain regions. A multiple regression model, also correcting for multiple comparisons, revealed that BMI was still negatively correlated with brain atrophy (FDR <5%), while DM2 and FPI were no longer associated with any volume differences. In an Analysis of Covariance (ANCOVA) model controlling for age, gender, and race, obese subjects with a high BMI (BMI > 30) showed atrophy in the frontal lobes, anterior cingulate gyrus, hippocampus, and thalamus compared with individuals with a normal BMI (18.5-25). Overweight subjects (BMI: 25-30) had atrophy in the basal ganglia and corona radiata of the WM. Overall brain volume did not differ between overweight and obese persons. Higher BMI was associated with lower brain volumes in overweight and obese elderly subjects. Obesity is therefore associated with detectable brain volume deficits in cognitively normal elderly subjects.
Collapse
Affiliation(s)
- Cyrus A. Raji
- Department of Pathology, University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania
- Department of Radiology, University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania
| | - April J. Ho
- Laboratory of Neuro Imaging, Department of Neurology, University of California Los Angeles, School of Medicine, Los Angeles, California
| | - Neelroop N. Parikshak
- Laboratory of Neuro Imaging, Department of Neurology, University of California Los Angeles, School of Medicine, Los Angeles, California
| | - James T. Becker
- Department of Psychiatry, University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania
- Department of Psychology, University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania
- Department of Neurology, University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania
| | - Oscar L. Lopez
- Department of Neurology, University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania
| | - Lewis H. Kuller
- Department of Epidemiology, University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania
| | - Xue Hua
- Laboratory of Neuro Imaging, Department of Neurology, University of California Los Angeles, School of Medicine, Los Angeles, California
| | - Alex D. Leow
- Laboratory of Neuro Imaging, Department of Neurology, University of California Los Angeles, School of Medicine, Los Angeles, California
| | - Arthur W. Toga
- Laboratory of Neuro Imaging, Department of Neurology, University of California Los Angeles, School of Medicine, Los Angeles, California
| | - Paul M. Thompson
- Laboratory of Neuro Imaging, Department of Neurology, University of California Los Angeles, School of Medicine, Los Angeles, California
| |
Collapse
|
36
|
Stein JL, Hua X, Lee S, Ho AJ, Leow AD, Toga AW, Saykin AJ, Shen L, Foroud T, Pankratz N, Huentelman MJ, Craig DW, Gerber JD, Allen AN, Corneveaux JJ, Dechairo BM, Potkin SG, Weiner MW, Thompson P. Voxelwise genome-wide association study (vGWAS). Neuroimage 2010; 53:1160-74. [PMID: 20171287 DOI: 10.1016/j.neuroimage.2010.02.032] [Citation(s) in RCA: 180] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2009] [Revised: 01/21/2010] [Accepted: 02/11/2010] [Indexed: 01/23/2023] Open
Abstract
The structure of the human brain is highly heritable, and is thought to be influenced by many common genetic variants, many of which are currently unknown. Recent advances in neuroimaging and genetics have allowed collection of both highly detailed structural brain scans and genome-wide genotype information. This wealth of information presents a new opportunity to find the genes influencing brain structure. Here we explore the relation between 448,293 single nucleotide polymorphisms in each of 31,622 voxels of the entire brain across 740 elderly subjects (mean age+/-s.d.: 75.52+/-6.82 years; 438 male) including subjects with Alzheimer's disease, Mild Cognitive Impairment, and healthy elderly controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We used tensor-based morphometry to measure individual differences in brain structure at the voxel level relative to a study-specific template based on healthy elderly subjects. We then conducted a genome-wide association at each voxel to identify genetic variants of interest. By studying only the most associated variant at each voxel, we developed a novel method to address the multiple comparisons problem and computational burden associated with the unprecedented amount of data. No variant survived the strict significance criterion, but several genes worthy of further exploration were identified, including CSMD2 and CADPS2. These genes have high relevance to brain structure. This is the first voxelwise genome wide association study to our knowledge, and offers a novel method to discover genetic influences on brain structure.
Collapse
Affiliation(s)
- Jason L Stein
- Laboratory of Neuro Imaging, Department of Neurology, University of California, Los Angeles School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
37
|
Zhan L, Leow AD, Jahanshad N, Chiang MC, Barysheva M, Lee AD, Toga AW, McMahon KL, de Zubicaray GI, Wright MJ, Thompson PM. How does angular resolution affect diffusion imaging measures? Neuroimage 2010; 49:1357-71. [PMID: 19819339 PMCID: PMC3086646 DOI: 10.1016/j.neuroimage.2009.09.057] [Citation(s) in RCA: 52] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2009] [Revised: 08/24/2009] [Accepted: 09/24/2009] [Indexed: 10/20/2022] Open
Abstract
A key question in diffusion imaging is how many diffusion-weighted images suffice to provide adequate signal-to-noise ratio (SNR) for studies of fiber integrity. Motion, physiological effects, and scan duration all affect the achievable SNR in real brain images, making theoretical studies and simulations only partially useful. We therefore scanned 50 healthy adults with 105-gradient high-angular resolution diffusion imaging (HARDI) at 4T. From gradient image subsets of varying size (6
Collapse
Affiliation(s)
- Liang Zhan
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, 635 Charles E. Young Drive South, Suite 225E, Los Angeles, CA 90095-7332, USA
| | | | | | | | | | | | | | | | | | | | | |
Collapse
|
38
|
Hua X, Lee S, Yanovsky I, Leow AD, Chou YY, Ho AJ, Gutman B, Toga AW, Jack CR, Bernstein MA, Reiman EM, Harvey DJ, Kornak J, Schuff N, Alexander GE, Weiner MW, Thompson PM. Optimizing power to track brain degeneration in Alzheimer's disease and mild cognitive impairment with tensor-based morphometry: an ADNI study of 515 subjects. Neuroimage 2009; 48:668-81. [PMID: 19615450 PMCID: PMC2971697 DOI: 10.1016/j.neuroimage.2009.07.011] [Citation(s) in RCA: 95] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2009] [Revised: 07/01/2009] [Accepted: 07/03/2009] [Indexed: 10/20/2022] Open
Abstract
Tensor-based morphometry (TBM) is a powerful method to map the 3D profile of brain degeneration in Alzheimer's disease (AD) and mild cognitive impairment (MCI). We optimized a TBM-based image analysis method to determine what methodological factors, and which image-derived measures, maximize statistical power to track brain change. 3D maps, tracking rates of structural atrophy over time, were created from 1030 longitudinal brain MRI scans (1-year follow-up) of 104 AD patients (age: 75.7+/-7.2 years; MMSE: 23.3+/-1.8, at baseline), 254 amnestic MCI subjects (75.0+/-7.2 years; 27.0+/-1.8), and 157 healthy elderly subjects (75.9+/-5.1 years; 29.1+/-1.0), as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI). To determine which TBM designs gave greatest statistical power, we compared different linear and nonlinear registration parameters (including different regularization functions), and different numerical summary measures derived from the maps. Detection power was greatly enhanced by summarizing changes in a statistically-defined region-of-interest (ROI) derived from an independent training sample of 22 AD patients. Effect sizes were compared using cumulative distribution function (CDF) plots and false discovery rate methods. In power analyses, the best method required only 48 AD and 88 MCI subjects to give 80% power to detect a 25% reduction in the mean annual change using a two-sided test (at alpha=0.05). This is a drastic sample size reduction relative to using clinical scores as outcome measures (619 AD/6797 MCI for the ADAS-Cog, and 408 AD/796 MCI for the Clinical Dementia Rating sum-of-boxes scores). TBM offers high statistical power to track brain changes in large, multi-site neuroimaging studies and clinical trials of AD.
Collapse
Affiliation(s)
- Xue Hua
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | - Suh Lee
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | - Igor Yanovsky
- Department of Mathematics, UCLA, Los Angeles, CA, USA
| | - Alex D. Leow
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
- Resnick Neuropsychiatric Hospital at UCLA, Los Angeles, CA, USA
| | - Yi-Yu Chou
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | - April J. Ho
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | - Boris Gutman
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | - Arthur W. Toga
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | | | | | - Eric M. Reiman
- Banner Alzheimer’s Institute, Department of Psychiatry, University of Arizona, Phoenix, AZ, USA
| | - Danielle J. Harvey
- Department of Public Health Sciences, UCD School of Medicine, Davis, CA, USA
| | - John Kornak
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, UCSF, San Francisco, CA, USA
| | - Norbert Schuff
- Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
| | | | - Michael W. Weiner
- Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
- Department of Medicine, UCSF, San Francisco, CA, USA
- Department of Psychiatry, UCSF, San Francisco, CA, USA
| | - Paul M. Thompson
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | | |
Collapse
|
39
|
Yanovsky I, Leow AD, Lee S, Osher SJ, Thompson PM. Comparing registration methods for mapping brain change using tensor-based morphometry. Med Image Anal 2009; 13:679-700. [PMID: 19631572 PMCID: PMC2773147 DOI: 10.1016/j.media.2009.06.002] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2008] [Revised: 04/27/2009] [Accepted: 06/11/2009] [Indexed: 10/20/2022]
Abstract
Measures of brain changes can be computed from sequential MRI scans, providing valuable information on disease progression for neuroscientific studies and clinical trials. Tensor-based morphometry (TBM) creates maps of these brain changes, visualizing the 3D profile and rates of tissue growth or atrophy. In this paper, we examine the power of different nonrigid registration models to detect changes in TBM, and their stability when no real changes are present. Specifically, we investigate an asymmetric version of a recently proposed Unbiased registration method, using mutual information as the matching criterion. We compare matching functionals (sum of squared differences and mutual information), as well as large-deformation registration schemes (viscous fluid and inverse-consistent linear elastic registration methods versus Symmetric and Asymmetric Unbiased registration) for detecting changes in serial MRI scans of 10 elderly normal subjects and 10 patients with Alzheimer's Disease scanned at 2-week and 1-year intervals. We also analyzed registration results when matching images corrupted with artificial noise. We demonstrated that the unbiased methods, both symmetric and asymmetric, have higher reproducibility. The unbiased methods were also less likely to detect changes in the absence of any real physiological change. Moreover, they measured biological deformations more accurately by penalizing bias in the corresponding statistical maps.
Collapse
Affiliation(s)
- Igor Yanovsky
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109
- University of California, Los Angeles, Department of Mathematics, Los Angeles, CA 90095
| | - Alex D. Leow
- Departments of Psychiatry and Bioengineering, University of Illinois Medical Center, Chicago, IL 60612
- University of California, Los Angeles, School of Medicine, Laboratory of Neuro Imaging, Los Angeles, CA 90095
| | - Suh Lee
- University of California, Los Angeles, School of Medicine, Laboratory of Neuro Imaging, Los Angeles, CA 90095
| | - Stanley J. Osher
- University of California, Los Angeles, Department of Mathematics, Los Angeles, CA 90095
| | - Paul M. Thompson
- University of California, Los Angeles, School of Medicine, Laboratory of Neuro Imaging, Los Angeles, CA 90095
| |
Collapse
|
40
|
Thompson P, Hua X, Yanovsky I, Leow AD, Lee S, Ho AJ, Parikshak N, Toga AW, Jack CR, Weiner MW, Thompson PM. IC‐P‐078: Tensor‐based morphometry as surrogate marker for Alzheimer's disease and mild cognitive impairment: Optimizing statistical power. Alzheimers Dement 2009. [DOI: 10.1016/j.jalz.2009.05.632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Paul Thompson
- University of California Los AngelesLos AngelesCAUSA
| | - Xue Hua
- University of California Los AngelesLos AngelesCAUSA
| | - Igor Yanovsky
- University of California Los AngelesLos AngelesCAUSA
| | - Alex D. Leow
- University of California Los AngelesLos AngelesCAUSA
| | - Suh Lee
- University of California Los AngelesLos AngelesCAUSA
| | - April J. Ho
- University of California Los AngelesLos AngelesCAUSA
| | | | | | | | | | | |
Collapse
|
41
|
Ho AJ, Raji CA, Parikshak NN, Becker JT, Lopez OL, Kuller LH, Hua X, Leow AD, Toga AW, Thompson PM. Brain Structure and Obesity. Neuroimage 2009. [DOI: 10.1016/s1053-8119(09)70976-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
|
42
|
Leow AD, Yanovsky I, Parikshak N, Hua X, Lee S, Toga AW, Jack CR, Bernstein MA, Britson PJ, Gunter JL, Ward CP, Borowski B, Shaw LM, Trojanowski JQ, Fleisher AS, Harvey D, Kornak J, Schuff N, Alexander GE, Weiner MW, Thompson PM. Alzheimer's disease neuroimaging initiative: a one-year follow up study using tensor-based morphometry correlating degenerative rates, biomarkers and cognition. Neuroimage 2009; 45:645-55. [PMID: 19280686 PMCID: PMC2696624 DOI: 10.1016/j.neuroimage.2009.01.004] [Citation(s) in RCA: 138] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Tensor-based morphometry can recover three-dimensional longitudinal brain changes over time by nonlinearly registering baseline to follow-up MRI scans of the same subject. Here, we compared the anatomical distribution of longitudinal brain structural changes, over 12 months, using a subset of the ADNI dataset consisting of 20 patients with Alzheimer's disease (AD), 40 healthy elderly controls, and 40 individuals with mild cognitive impairment (MCI). Each individual longitudinal change map (Jacobian map) was created using an unbiased registration technique, and spatially normalized to a geometrically-centered average image based on healthy controls. Voxelwise statistical analyses revealed regional differences in atrophy rates, and these differences were correlated with clinical measures and biomarkers. Consistent with prior studies, we detected widespread cerebral atrophy in AD, and a more restricted atrophic pattern in MCI. In MCI, temporal lobe atrophy rates were correlated with changes in mini-mental state exam (MMSE) scores, clinical dementia rating (CDR), and logical/verbal learning memory scores. In AD, temporal atrophy rates were correlated with several biomarker indices, including a higher CSF level of p-tau protein, and a greater CSF tau/beta amyloid 1-42 (ABeta42) ratio. Temporal lobe atrophy was significantly faster in MCI subjects who converted to AD than in non-converters. Serial MRI scans can therefore be analyzed with nonlinear image registration to relate ongoing neurodegeneration to a variety of pathological biomarkers, cognitive changes, and conversion from MCI to AD, tracking disease progression in 3-dimensional detail.
Collapse
Affiliation(s)
- Alex D Leow
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095-1769, USA.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
43
|
Hua X, Leow AD, Levitt JG, Caplan R, Thompson PM, Toga AW. Detecting brain growth patterns in normal children using tensor-based morphometry. Hum Brain Mapp 2009; 30:209-19. [PMID: 18064588 DOI: 10.1002/hbm.20498] [Citation(s) in RCA: 67] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Previous magnetic resonance imaging (MRI)-based volumetric studies have shown age-related increases in the volume of total white matter and decreases in the volume of total gray matter of normal children. Recent adaptations of image analysis strategies enable the detection of human brain growth with improved spatial resolution. In this article, we further explore the spatio-temporal complexity of adolescent brain maturation with tensor-based morphometry. By utilizing a novel non-linear elastic intensity-based registration algorithm on the serial structural MRI scans of 13 healthy children, individual Jacobian growth maps are generated and then registered to a common anatomical space. Statistical analyses reveal significant tissue growth in cerebral white matter, contrasted with gray matter loss in parietal, temporal, and occipital lobe. In addition, a linear regression with age and gender suggests a slowing down of the growth rate in regions with the greatest white matter growth. We demonstrate that a tensor-based Jacobian map is a sensitive and reliable method to detect regional tissue changes during development.
Collapse
Affiliation(s)
- Xue Hua
- Laboratory of Neuro Imaging, Brain Mapping Division, Department of Neurology, University of California Los Angeles School of Medicine, Los Angeles, CA 90095-7334, USA
| | | | | | | | | | | |
Collapse
|
44
|
Zhan L, Leow AD, Zhu S, Baryshev M, Toga AW, McMahon KL, de Zubicaray GI, Wright MJ, Thompson PM. A novel measure of fractional anisotropy based on the tensor distribution function. Med Image Comput Comput Assist Interv 2009; 12:845-52. [PMID: 20426067 DOI: 10.1007/978-3-642-04268-3_104] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Fractional anisotropy (FA), a very widely used measure of fiber integrity based on diffusion tensor imaging (DTI), is a problematic concept as it is influenced by several quantities including the number of dominant fiber directions within each voxel, each fiber's anisotropy, and partial volume effects from neighboring gray matter. With High-angular resolution diffusion imaging (HARDI) and the tensor distribution function (TDF), one can reconstruct multiple underlying fibers per voxel and their individual anisotropy measures by representing the diffusion profile as a probabilistic mixture of tensors. We found that FA, when compared with TDF-derived anisotropy measures, correlates poorly with individual fiber anisotropy, and may sub-optimally detect disease processes that affect myelination. By contrast, mean diffusivity (MD) as defined in standard DTI appears to be more accurate. Overall, we argue that novel measures derived from the TDF approach may yield more sensitive and accurate information than DTI-derived measures.
Collapse
Affiliation(s)
- Liang Zhan
- Laboratory of Neuroimaging, Dept. of Neurology, University of California, Los Angeles, USA
| | | | | | | | | | | | | | | | | |
Collapse
|
45
|
Leow AD, Zhu S, Zhan L, McMahon K, de Zubicaray GI, Meredith M, Wright MJ, Toga AW, Thompson PM. The tensor distribution function. Magn Reson Med 2009; 61:205-14. [PMID: 19097208 PMCID: PMC2770429 DOI: 10.1002/mrm.21852] [Citation(s) in RCA: 78] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2007] [Accepted: 09/17/2008] [Indexed: 12/31/2022]
Abstract
Diffusion weighted magnetic resonance imaging is a powerful tool that can be employed to study white matter microstructure by examining the 3D displacement profile of water molecules in brain tissue. By applying diffusion-sensitized gradients along a minimum of six directions, second-order tensors (represented by three-by-three positive definite matrices) can be computed to model dominant diffusion processes. However, conventional DTI is not sufficient to resolve more complicated white matter configurations, e.g., crossing fiber tracts. Recently, a number of high-angular resolution schemes with more than six gradient directions have been employed to address this issue. In this article, we introduce the tensor distribution function (TDF), a probability function defined on the space of symmetric positive definite matrices. Using the calculus of variations, we solve the TDF that optimally describes the observed data. Here, fiber crossing is modeled as an ensemble of Gaussian diffusion processes with weights specified by the TDF. Once this optimal TDF is determined, the orientation distribution function (ODF) can easily be computed by analytic integration of the resulting displacement probability function. Moreover, a tensor orientation distribution function (TOD) may also be derived from the TDF, allowing for the estimation of principal fiber directions and their corresponding eigenvalues.
Collapse
Affiliation(s)
- A D Leow
- Neuropsychiatric Hospital and LONI (Laboratory of NeuroImaging), University of California, Los Angeles, California 90095, USA.
| | | | | | | | | | | | | | | | | |
Collapse
|
46
|
Hua X, Leow AD, Parikshak N, Lee S, Chiang MC, Toga AW, Jack CR, Weiner MW, Thompson PM. Tensor-based morphometry as a neuroimaging biomarker for Alzheimer's disease: an MRI study of 676 AD, MCI, and normal subjects. Neuroimage 2008; 43:458-69. [PMID: 18691658 DOI: 10.1016/j.neuroimage.2008.07.013] [Citation(s) in RCA: 242] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2008] [Accepted: 07/11/2008] [Indexed: 10/21/2022] Open
Abstract
In one of the largest brain MRI studies to date, we used tensor-based morphometry (TBM) to create 3D maps of structural atrophy in 676 subjects with Alzheimer's disease (AD), mild cognitive impairment (MCI), and healthy elderly controls, scanned as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI). Using inverse-consistent 3D non-linear elastic image registration, we warped 676 individual brain MRI volumes to a population mean geometric template. Jacobian determinant maps were created, revealing the 3D profile of local volumetric expansion and compression. We compared the anatomical distribution of atrophy in 165 AD patients (age: 75.6+/-7.6 years), 330 MCI subjects (74.8+/-7.5), and 181 controls (75.9+/-5.1). Brain atrophy in selected regions-of-interest was correlated with clinical measurements--the sum-of-boxes clinical dementia rating (CDR-SB), mini-mental state examination (MMSE), and the logical memory test scores - at voxel level followed by correction for multiple comparisons. Baseline temporal lobe atrophy correlated with current cognitive performance, future cognitive decline, and conversion from MCI to AD over the following year; it predicted future decline even in healthy subjects. Over half of the AD and MCI subjects carried the ApoE4 (apolipoprotein E4) gene, which increases risk for AD; they showed greater hippocampal and temporal lobe deficits than non-carriers. ApoE2 gene carriers--1/6 of the normal group--showed reduced ventricular expansion, suggesting a protective effect. As an automated image analysis technique, TBM reveals 3D correlations between neuroimaging markers, genes, and future clinical changes, and is highly efficient for large-scale MRI studies.
Collapse
Affiliation(s)
- Xue Hua
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | | | | | | | | | | | | | | | | | | |
Collapse
|
47
|
Yanovsky I, Thompson PM, Osher S, Leow AD. Asymmetric and Symmetric Unbiased Image Registration: Statistical Assessment of Performance. Conf Comput Vis Pattern Recognit Workshops 2008; 2008. [PMID: 29152411 DOI: 10.1109/cvprw.2008.4562988] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Measures of brain changes can be computed from sequential MRI scans, providing valuable information on disease progression for neuroscientific studies and clinical trials. Tensor-based morphometry (TBM) creates maps of these brain changes, visualizing the 3D profile and rates of tissue growth or atrophy. In this paper, we examine the power of different nonrigid registration models to detect changes in TBM, and their stability when no real changes are present. Specifically, we investigate an asymmetric version of a recently proposed unbiased registration method, using mutual information as the matching criterion. We compare matching functionals (sum of squared differences and mutual information), as well as large deformation registration schemes (viscous fluid registration versus symmetric and asymmetric unbiased registration) for detecting changes in serial MRI scans of 10 elderly normal subjects and 10 patients with Alzheimer's Disease scanned at 2-week and 1-year intervals. We demonstrated that the unbiased methods, both symmetric and asymmetric, have higher reproducibility. The unbiased methods were also less likely to detect changes in the absence of any real physiological change. Moreover, they measured biological deformations more accurately by penalizing bias in the corresponding statistical maps.
Collapse
Affiliation(s)
- Igor Yanovsky
- Department of Mathematics, University of California, Los Angeles, CA 90095
| | - Paul M Thompson
- Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA 90095
| | - Stanley Osher
- Department of Mathematics, University of California, Los Angeles, CA 90095
| | - Alex D Leow
- Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA 90095
| |
Collapse
|
48
|
Chiang MC, Leow AD, Klunder AD, Dutton RA, Barysheva M, Rose SE, McMahon KL, de Zubicaray GI, Toga AW, Thompson PM. Fluid registration of diffusion tensor images using information theory. IEEE Trans Med Imaging 2008; 27:442-456. [PMID: 18390342 PMCID: PMC2770435 DOI: 10.1109/tmi.2007.907326] [Citation(s) in RCA: 60] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
We apply an information-theoretic cost metric, the symmetrized Kullback-Leibler (sKL) divergence, or J-divergence, to fluid registration of diffusion tensor images. The difference between diffusion tensors is quantified based on the sKL-divergence of their associated probability density functions (PDFs). Three-dimensional DTI data from 34 subjects were fluidly registered to an optimized target image. To allow large image deformations but preserve image topology, we regularized the flow with a large-deformation diffeomorphic mapping based on the kinematics of a Navier-Stokes fluid. A driving force was developed to minimize the J-divergence between the deforming source and target diffusion functions, while reorienting the flowing tensors to preserve fiber topography. In initial experiments, we showed that the sKL-divergence based on full diffusion PDFs is adaptable to higher-order diffusion models, such as high angular resolution diffusion imaging (HARDI). The sKL-divergence was sensitive to subtle differences between two diffusivity profiles, showing promise for nonlinear registration applications and multisubject statistical analysis of HARDI data.
Collapse
Affiliation(s)
- Ming-Chang Chiang
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095 USA
| | - Alex D. Leow
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095 USA
| | - Andrea D. Klunder
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095 USA
| | - Rebecca A. Dutton
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095 USA
| | - Marina Barysheva
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095 USA
| | - Stephen E. Rose
- Centre for Magnetic Resonance, University of Queensland, 4072 Brisbane, Australia
| | - Katie L. McMahon
- Centre for Magnetic Resonance, University of Queensland, 4072 Brisbane, Australia
| | | | - Arthur W. Toga
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095 USA
| | - Paul M. Thompson
- P. M. Thompson is with the Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, 710 Westwood Plaza, Los Angeles, CA 90095 USA (e-mail: )
| |
Collapse
|
49
|
Hua X, Leow AD, Lee S, Klunder AD, Toga AW, Lepore N, Chou YY, Brun C, Chiang MC, Barysheva M, Jack CR, Bernstein MA, Britson PJ, Ward CP, Whitwell JL, Borowski B, Fleisher AS, Fox NC, Boyes RG, Barnes J, Harvey D, Kornak J, Schuff N, Boreta L, Alexander GE, Weiner MW, Thompson PM. 3D characterization of brain atrophy in Alzheimer's disease and mild cognitive impairment using tensor-based morphometry. Neuroimage 2008; 41:19-34. [PMID: 18378167 DOI: 10.1016/j.neuroimage.2008.02.010] [Citation(s) in RCA: 116] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2007] [Revised: 02/06/2008] [Accepted: 02/11/2008] [Indexed: 10/22/2022] Open
Abstract
Tensor-based morphometry (TBM) creates three-dimensional maps of disease-related differences in brain structure, based on nonlinearly registering brain MRI scans to a common image template. Using two different TBM designs (averaging individual differences versus aligning group average templates), we compared the anatomical distribution of brain atrophy in 40 patients with Alzheimer's disease (AD), 40 healthy elderly controls, and 40 individuals with amnestic mild cognitive impairment (aMCI), a condition conferring increased risk for AD. We created an unbiased geometrical average image template for each of the three groups, which were matched for sex and age (mean age: 76.1 years+/-7.7 SD). We warped each individual brain image (N=120) to the control group average template to create Jacobian maps, which show the local expansion or compression factor at each point in the image, reflecting individual volumetric differences. Statistical maps of group differences revealed widespread medial temporal and limbic atrophy in AD, with a lesser, more restricted distribution in MCI. Atrophy and CSF space expansion both correlated strongly with Mini-Mental State Exam (MMSE) scores and Clinical Dementia Rating (CDR). Using cumulative p-value plots, we investigated how detection sensitivity was influenced by the sample size, the choice of search region (whole brain, temporal lobe, hippocampus), the initial linear registration method (9- versus 12-parameter), and the type of TBM design. In the future, TBM may help to (1) identify factors that resist or accelerate the disease process, and (2) measure disease burden in treatment trials.
Collapse
Affiliation(s)
- Xue Hua
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, Los Angeles, CA 90095-1769, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
50
|
Foland LC, Altshuler LL, Sugar CA, Lee AD, Leow AD, Townsend J, Narr KL, Asuncion DM, Toga AW, Thompson PM. Increased volume of the amygdala and hippocampus in bipolar patients treated with lithium. Neuroreport 2008; 19:221-4. [PMID: 18185112 PMCID: PMC3299336 DOI: 10.1097/wnr.0b013e3282f48108] [Citation(s) in RCA: 146] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Previous structural neuroimaging studies of bipolar disorder have reported conflicting findings in limbic structures. Medication heterogeneity of patient samples may have contributed to these inconsistencies. Using structural magnetic resonance imaging we assessed whether lithium treatment was associated with differences in amygdala and hippocampal volumes in a sample of bipolar adults. A total of 49 magnetic resonance imaging scans were collected from patients who were currently treated with or without lithium. Amygdala and hippocampal volumes were analyzed using tensor-based morphometry. Statistical between-group comparisons of deformation maps showed that patients treated with lithium exhibited significantly increased volumes of the amygdala and hippocampus compared with patients who were not taking lithium. Our findings may help to explain previous inconsistencies in the bipolar literature.
Collapse
Affiliation(s)
- Lara C. Foland
- Laboratory of NeuroImaging, Department of Neurology, University of California, Los Angeles, California, USA
| | - Lori L. Altshuler
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, USA
- Department of Psychiatry, VA Greater Los Angeles Healthcare System, West Los Angeles Healthcare Center, Los Angeles, California, USA
| | - Catherine A. Sugar
- Department of Biostatistics, University of California, Los Angeles, California, USA
| | - Agatha D. Lee
- Laboratory of NeuroImaging, Department of Neurology, University of California, Los Angeles, California, USA
| | - Alex D. Leow
- Laboratory of NeuroImaging, Department of Neurology, University of California, Los Angeles, California, USA
| | - Jennifer Townsend
- Ahmanson-Lovelace Brain Mapping Center, UCLA School of Medicine, Los Angeles, California, USA
| | - Katherine L. Narr
- Laboratory of NeuroImaging, Department of Neurology, University of California, Los Angeles, California, USA
| | - Dina M. Asuncion
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, USA
| | - Arthur W. Toga
- Laboratory of NeuroImaging, Department of Neurology, University of California, Los Angeles, California, USA
| | - Paul M. Thompson
- Laboratory of NeuroImaging, Department of Neurology, University of California, Los Angeles, California, USA
| |
Collapse
|